# prakhar1989/awesome-courses

Computer Science 1 year ago 40.3k

## Nov 23rd - Nov 29th, 2020

## Courses

### Misc

**Fundamentals of Computer Networks**

*CMU*

- This is an introductory course on Networking for graduate students. It follows a top-down approach to teaching Computer Networks, so it starts with the Application layer which most of the students are familiar with and as the course unravels we learn more about transport, network and link layers of the protocol stack.
- As far as prerequisites are concerned - basic computer, programming and probability theory background is required.
- The course site contains links to the lecture videos, reading material and assignments.

## Oct 14th - Oct 20th, 2019

## Courses

### Systems

**Embedded Systems using the Renesas RX63N Processor**

*University of North Carolina at Charlotte*

- Introduction to designing microcontroller-based embedded computer systems using assembly and C programs. Examination of Real-time Operating Systems and their impact on performance. Computer engineering applications will be emphasized.
- The goal of this course is to solidify and build upon a student’s knowledge of computer organization by presenting hands-on experience with microcontrollers. Students will also examine a few sensors that are used in commercial and medical products and learn how to interface them in a microcontroller system.
- Lecture Videos
- Lecture Notes

## Oct 22nd - Oct 28th, 2018

## Courses

### Misc

**Quantum Information Processing**

*Cornell University*

- Hardware that exploits quantum phenomena can dramatically alter the nature of computation. Though constructing a working quantum computer is a formidable technological challenge, there has been much recent experimental progress. In addition, the theory of quantum computation is of interest in itself, offering strikingly different perspectives on the nature of computation and information, as well as providing novel insights into the conceptual puzzles posed by the quantum theory. The course is intended both for physicists, unfamiliar with computational complexity theory or cryptography, and also for computer scientists and mathematicians, unfamiliar with quantum mechanics. The prerequisites are familiarity (and comfort) with finite dimensional vector spaces over the complex numbers, some standard group theory, and ability to count in binary.
- Syllabus
- Lectures

## Courses

### Algorithms

**Introduction to Analysis of Algorithms**

*Cornell University*

- This course develops techniques used in the design and analysis of algorithms, with an emphasis on problems arising in computing applications. Example applications are drawn from systems and networks, artificial intelligence, computer vision, data mining, and computational biology. This course covers four major algorithm design techniques (greedy algorithms, divide and conquer, dynamic programming, and network flow), computability theory focusing on undecidability, computational complexity focusing on NP-completeness, and algorithmic techniques for intractable problems, including identification of structured special cases, approximation algorithms, and local search heuristics.
- Lectures
- Syllabus

## Courses

### CS Theory

**Theory of Computing**

*Cornell University*

- This graduate course gives a broad introduction to complexity theory, including classical results and recent developments. Complexity theory aims to understand the power of efficient computation (when computational resources like time and space are limited). Many compelling conceptual questions arise in this context. Most of these questions are (surprisingly?) difficult and far from being resolved. Nevertheless, a lot of progress has been made toward understanding them (and also why they are difficult). We will learn about these advances in this course. A theme will be combinatorial constructions with random-like properties, e.g., expander graphs and error-correcting codes. Some examples:
- Is finding a solution inherently more difficult than verifying it?
- Do more computational resources mean more computing power?
- Is it easier to find approximate solutions than exact ones?
- Are randomized algorithms more powerful than deterministic ones?
- Is it easier to solve problems in the average case than in the worst case?
- Are quantum computers more powerful than classical ones?

- Syllabus
- Lectures
- Assignments

## Jun 4th - Jun 10th, 2018

## Courses

### Computer Graphics

**Introduction to Computer Graphics**

*Brown University*

- This course offers an in-depth exploration of fundamental concepts in 2D and 3D computer graphics. It introduces 2D raster graphics techniques, including scan conversion, simple image processing, interaction techniques and user interface design. The bulk of the course is devoted to 3D modeling, geometric transformations, and 3D viewing and rendering.
- Lectures
- Labs
- Demos

## May 21st - May 27th, 2018

## Courses

### Misc

**Intro to Game Developement**

*Harvard University*

- This course picks up where Harvard College’s CS50 leaves off, focusing on the development of 2D and 3D interactive games. Students explore the design of such childhood games as Super Mario Bros., Legend of Zelda, and Portal in a quest to understand how video games themselves are implemented. Via lectures and hands-on projects, the course explores principles of 2D and 3D graphics, animation, sound, and collision detection using frameworks like Unity and LÖVE 2D, as well as languages like Lua and C#. By class’s end, students will have programmed several of their own games and gained a thorough understanding of the basics of game design and development.
- Assignments
- Lecture Videos

## Mar 19th - Mar 25th, 2018

## Courses

### Security

**Computer and Network Security**

*Stanford*

- Principles of computer systems security. Attack techniques and how to defend against them. Topics include: network attacks and defenses, operating system holes, application security (web, email, databases), viruses, social engineering attacks, privacy, and digital rights management. Course projects focus on building reliable code. Recommended: Basic Unix. Primarily intended for seniors and first-year graduate students.

## Feb 26th - Mar 4th, 2018

## Courses

### Misc

**Datacenter Networks and Services**

*Cornell University*

- CS6452 focuses on datacenter networks and services. The emerging demand for web services and cloud computing have created need for large scale data centers. The hardware and software infrastructure for datacenters critically determines the functionality, performance, cost and failure tolerance of applications running on that datacenter. This course will examine design alternatives for both the hardware (networking) infrastructure, and the software infrastructure for datacenters.
- Syllabus
- Lectures

## Nov 27th - Dec 3rd, 2017

## Courses

### Misc

**Introduction to Robotics**

*Stanford University*

- The purpose of this course is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control.

## Oct 23rd - Oct 29th, 2017

## Courses

### Systems

**Cloud Computing (ICS)**

*Carnegie-Mellon University*

- This project-based on-line course focuses on skill building across various aspects of cloud computing. We cover conceptual topics and provide hands-on experience through projects utilizing public cloud infrastructures (Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP)). The adoption of cloud computing services continues to grow across a variety of organizations and in many domains. Simply, cloud computing is the delivery of computing as a service over a network, whereby distributed resources and services are rented, rather than owned, by an end user as a utility.
- For the projects, students will work with Amazon Web Services, Microsoft Azure and Google Cloud Platform, use them to rent and provision compute resources and then program and deploy applications that run on these resources. Students will develop and evaluate virtual machine (VM) and container scaling, elasticity and load balancing solutions. In addition, students will work with cloud storage systems and learn to develop different applications using batch, iterative and stream processing frameworks. 15-619 students will have to complete an extra project which entails designing and implementing a complete web-service solution for querying big data. For the extra project, the student teams are evaluated based on the cost and performance of their web service.
- Lecture Notes
- Videos

## Sep 4th - Sep 10th, 2017

## Courses

### Systems

**Database Systems**

*Carnegie-Mellon University*

- This course covers fundamental topics of DBMS, compared to CMU 15-721.
- Assignments
- Lecture Videos
- Readings

## Jul 17th - Jul 23rd, 2017

## Courses

### Introduction to CS

**Programming Practice Using Scala**

*KAIST*

- This course introduces basic concepts of programming and computer science, such as dynamic and static typing, dynamic memory allocation, objects and methods, binary representation of numbers, using an editor and compiler from the command line, running programs with arguments from the command line, using libraries, and the use of basic data structures such as arrays, lists, sets, and maps. We will use Scala for this course.
- [Lectures] (http://otfried.org/courses/cs109/index.html)
- [Assignments] (http://otfried.org/courses/cs109/index.html)

## Mar 20th - Mar 26th, 2017

## Courses

### Systems

**Introduction to Computer Architecture**

*CMU*

- Very comprehensive material on Computer Architecture - definitely more than just "introduction". Online material is very user-friendly, even the recitation videos available online. This is the Spring'15 version by Prof. Onur Mutlu
- Lectures and Recitation
- Homeworks 7 HWs with answer set as well
- Readings

## Mar 13th - Mar 19th, 2017

## Courses

### Systems

**Parallel Computer Architecture and Programming**

*Carnegie-Mellon University*

- The goal of this course is to provide a deep understanding of the fundamental principles and engineering trade-offs involved in designing modern parallel computing systems as well as to teach parallel programming techniques necessary to effectively utilize these machines. Because writing good parallel programs requires an understanding of key machine performance characteristics, this course will cover both parallel hardware and software design.
- Assignments
- Lecture Notes
- Lecture Videos
- Readings

**Computer Organization & Systems**

*Stanford University*

- CS107 is the third course in Stanford's introductory programming sequence. The course will work from the C programming language down to the microprocessor to de-mystify the machine. With a complete understanding of how computer systems execute programs and manipulate data, you will become a more effective programmer, especially in dealing with issues of debugging, performance, portability, and robustness.
- Lecture Videos
- Assignments

**Cloud Computing**

*Cornell University*

- Taught by one of the stalwarts of this field, Prof Ken Birman, this course has a fantastic set of slides that one can go through. The Prof's book is also a gem and recommended as a must read in Google's tutorial on Distributed System Design
- Slides

**UNIX System Programming (formerly UNIX Tools)**

*CUNY Hunter College*

- A course that is mostly about writing programs against the UNIX API, covering all of the basic parts of the kernel interface and libraries, including files, processes, terminal control, signals, and threading.

**Parallel Computing**

*CUNY Hunter College*

- The course is an introduction to parallel algorithms and parallel programming in C and C++, using the Message Passing Interface (MPI) and the OpenMP application programming interface. It also includes a brief introduction to parallel architectures and interconnection networks. It is both theoretical and practical, including material on design methodology, performance analysis, and mathematical concepts, as well as details on programming using MPI and OpenMP.

**Programming for Performance**

*University of Waterloo*

- Learn techniques for profiling, rearchitecting, and implementing software systems that can handle industrial-sized inputs, and to design and build critical software infrastructure. Learn performance optimization through parallelization, multithreading, async I/O, vectorization and GPU programming, and distributed computing.
- Lecture slides

## Courses

### Programming Languages / Compilers

**Purely Functional Data Structures In Elm**

*University of Chicago*

- This course teaches functional reactive programming and purely functional data structures based on Chris Okazaki's book and using the Elm programming language.
- Lectures
- Assignments

## Mar 6th - Mar 12th, 2017

## Courses

### Machine Learning

**Tensorflow for Deep Learning Research**

*Stanford University*

- This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.
- Assignmentsstars10.2k available on Github.

## Courses

### Systems

**Distributed Systems**

*Univ of Illinois, Urbana-Champaign*

- Brilliant set of lectures and reading material covering fundamental concepts in distributed systems such as Vector clocks, Consensus and Paxos. This is the 2016 version by Prof Indranil Gupta.
- Lectures
- Assignments

## Feb 20th - Feb 26th, 2017

## Courses

### Algorithms

**Data Structures**

*UC Berkeley*

- In this course, you will study advanced programming techniques including data structures, encapsulation, abstract data types, interfaces, and algorithms for sorting and searching, and you will get a taste of “software engineering”—the design and implementation of large programs.
- Full Lecture Materials Lecture of Spring 2016. This website contains full matrials including video links, labs, homeworks, projects. Very good for self-learner. Also a good start for Java. And it includes some other useful resources for Java Documentation, Data Structure Resources, Git/GitHub and Java Development Resources. Resources
- Labs The link to labs and projects is included in the website.
- Lecture Videos

## Feb 6th - Feb 12th, 2017

## Courses

### Systems

**Distributed Systems**

*University of Washington*

- CSEP552 is a graduate course on distributed systems. Distributed systems have become central to many aspects of how computers are used, from web applications to e-commerce to content distribution. This course will cover abstractions and implementation techniques for the construction of distributed systems, including client server computing, the web, cloud computing, peer-to-peer systems, and distributed storage systems. Topics will include remote procedure call, maintaining consistency of distributed state, fault tolerance, high availability, and other topics. As we believe the best way to learn the material is to build it, there will be a series of hands-on programming projects.
- Lectures of a previous session are available to watch.

## Courses

### Machine Learning

**Deep Learning for Natural Language Processing**

*University of Oxford*

- This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimisation algorithms. The course covers a range of applications of neural networks in NLP including analysing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. This course is organised by Phil Blunsom and delivered in partnership with the
**DeepMind Natural Language Research Group**. - Lecturesstars15.3k
- Assignments are available on the organisation page titled as "practicals"

## Courses

### Misc

**Object Oriented System Design**

*Stanford*

- Software design and construction in the context of large OOP libraries. Taught in Java. Topics: OOP design, design patterns, testing, graphical user interface (GUI) OOP libraries, software engineering strategies, approaches to programming in teams.

## Jan 9th - Jan 15th, 2017

## Courses

### Systems

**Introduction to Database Systems**

*UC Berkeley*

- In the project assignments in CS186, you will write a basic database management system called SimpleDB. For this project, you will focus on implementing the core modules required to access stored data on disk; in future projects, you will add support for various query processing operators, as well as transactions, locking, and concurrent queries.
- Lecture Videos
- Lecture Notes
- Projects

## Dec 19th - Dec 25th, 2016

## Courses

### Programming Languages / Compilers

**Introduction to Programming Languages**

*Swathmore College*

**Principles of Compiler Design**

*Swathmore College*

- Modelled after the influential paper on incremental approach to compiler design, this course teaches how to build a compiler in OCaml
- Course on Github
- Notesstars37

## Dec 5th - Dec 11th, 2016

## Courses

### Systems

**Systems Programming (Spring 2016)**

*Univ of Illinois, Urbana-Champaign*

- System programming refers to writing code that tasks advantage of operating system support for programmers. This course is designed to introduce you to system programming. By the end of this course, you should be proficient at writing programs that take full advantage of operating system support. To be concrete, we need to fix an operating system and we need to choose a programming language for writing programs. We chose the C language running on a Linux/UNIX operating system (which implements the POSIX standard interface between the programmer and the OS).
- Assignments
- Labs
- Github Page
- Crowd Sourced Book

## Nov 14th - Nov 20th, 2016

## Courses

### Systems

**Computation Structures**

*MIT*

- Introduces architecture of digital systems, emphasizing structural principles common to a wide range of technologies. Multilevel implementation strategies; definition of new primitives (e.g., gates, instructions, procedures, processes) and their mechanization using lower-level elements. Analysis of potential concurrency; precedence constraints and performance measures; pipelined and multidimensional systems. Instruction set design issues; architectural support for contemporary software structures. 4 Engineering Design Points. 6.004 offers an introduction to the engineering of digital systems. Starting with MOS transistors, the course develops of series of building blocks logic gates, combinational and sequential circuits, finite-state machines, computers and finally complete systems. Both hardware and software mechanisms are explored through a series of design examples.
- Youtube Playlist
- Lecture Notes
- Labs-Assignments

## Courses

### Introduction to CS

**Understanding Computers and the Internet**

*Harvard University Extension College*

## Oct 24th - Oct 30th, 2016

## Courses

### Systems

**Computer System Organization and Programming**

*Cornell University*

- CS3410 provides an introduction to computer organization, systems programming and the hardware/software interface. Topics include instruction sets, computer arithmetic, datapath design, data formats, addressing modes, memory hierarchies including caches and virtual memory, I/O devices, bus-based I/O systems, and multicore architectures. Students learn assembly language programming and design a pipelined RISC processor.
- Lectures
- Assignments

## Oct 17th - Oct 23rd, 2016

## Courses

### Systems

**Introduction to Operating Systems**

*SUNY University at Buffalo, NY*

- This course is an introduction to operating system design and implementation. We study operating systems because they are examples of mature and elegant solutions to a difficult design problem: how to safely and efficiently share system resources and provide abstractions useful to applications.
- For the processor, memory, and disks, we discuss how the operating system allocates each resource and explore the design and implementation of related abstractions. We also establish techniques for testing and improving system performance and introduce the idea of hardware virtualization. Programming assignments provide hands-on experience with implementing core operating system components in a realistic development environment. Course by Dr.Geoffrey Challen
- Syllabus
- Slides
- Video lectures
- Assignments
- Old Exams

## Oct 3rd - Oct 9th, 2016

## Courses

### Systems

**Operating Systems**

*NYU*

- NYU's operating system course. It's a fundamental course focusing basic ideas of operating systems, including memory management, process shceduling, file system, ect. It also includes some recommended reading materials. What's more, there are a series of hands-on lab materials, helping you easily understand OS.
- Assignments
- Lectures
- Old Exams

## Courses

### Programming Languages / Compilers

**Introduction to Compilers**

*Univ of Maryland*

- The goal of CMSC 430 is to arm students with the ability to design, implement, and extend a programming language. Throughout the course, students will design and implement several related languages, and will explore parsing, syntax querying, dataflow analysis, compilation to bytecode, type systems, and language interoperation.
- Lecture Notes
- Assignments

**Compiler construction**

*Stanford University*

- CS143 is a Stanford's course in the practical and theoretical aspects of compiler construction.
- Home
- Syllabus
- Lectures
- Assignments
- CS143 - 2011

## Courses

### Algorithms

**Introduction to Competitive Programming**

*Stanford University*

- Fantastic repository of theory and practice problems across various topics for students who are interested to participate in ACM-ICPC.
- Lectures and Assignmentsstars1.5k

**Advanced Algorithms**

*Harvard University*

- CS 224 is an advanced course in algorithm design, and topics we will cover include the word RAM model, data structures, amortization, online algorithms, linear programming, semidefinite programming, approximation algorithms, hashing, randomized algorithms, fast exponential time algorithms, graph algorithms, and computational geometry.
- Lecture Videos (Youtube)
- Assignments

**A Second Course in Algorithms**

*Stanford University*

- Algorithms for network optimization: max-flow, min-cost flow, matching, assignment, and min-cut problems. Introduction to linear programming. Use of LP duality for design and analysis of algorithms. Approximation algorithms for NP-complete problems such as Steiner Trees, Traveling Salesman, and scheduling problems. Randomized algorithms. Introduction to online algorithms.
- Lecture Notes, Videos & Assignments (Youtube)

## Courses

### CS Theory

**Introduction to Scientific Computing**

*Cornell University*

- In this one-semester survey course, we introduce numerical methods for solving linear and nonlinear equations, interpolating data, computing integrals, and solving differential equations, and we describe how to use these tools wisely (we hope!) when solving scientific problems.
- Syllabus
- Lectures
- Assignments

**Information Retrieval**

*Cornell University*

- Studies the methods used to search for and discover information in large-scale systems. The emphasis is on information retrieval applied to textual materials, but there is some discussion of other formats.The course includes techniques for searching, browsing, and filtering information and the use of classification systems and thesauruses. The techniques are illustrated with examples from web searching and digital libraries.
- Syllabus
- Lectures
- Assignments

## Courses

### Introduction to CS

**Computer Science I and II for Hackers**

*University of Utah*

- An intro course in the spirit of SICP designed by Professor Matthew Flatt (one of the lead designers of Racket and author of HtDP). Mostly Racket and C, and a bit of Java, with explanations on how high level functional programming concepts relate to the design of OOP programs. Do this one before SICP if SICP is a bit too much...
- Lectures and Assignments 1
- Lectures and Assignments 2
- Textbook
- Racket Language

## Courses

### Machine Learning

**Machine Learning**

*Columbia University*

- Course taught by Tony Jebara introduces topics in Machine Learning for both generative and discriminative estimation. Material will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods.
- Lectures and Assignments

**Statistical and Discrete Methods for Scientific Computing**

*University of Texas*

- Practical course in applying modern statistical techniques to real data, particularly bioinformatic data and large data sets. The emphasis is on efficient computation and concise coding, mostly in MATLAB and C++.

## Courses

### Security

**Computer Security**

*UC Berkeley*

- Introduction to computer security. Cryptography, including encryption, authentication, hash functions, cryptographic protocols, and applications. Operating system security, access control. Network security, firewalls, viruses, and worms. Software security, defensive programming, and language-based security. Case studies from real-world systems.

**Security Modeling and Analysis**

*Stanford*

- The course will cover a variety of contemporary network protocols and other systems with security properties. The course goal is to give students hands-on experience in using automated tools and related techniques to analyze and evaluate security mechanisms. To understand security properties and requirements, we will look at several network protocols and their properties, including secrecy, authentication, key establishment, and fairness. In parallel, the course will look at several models and tools used in security analysis and examine their advantages and limitations. In addition to fully automated finite-state model checking techniques, we will also study other approaches, such as constraint solving, process algebras, protocol logics, probabilistic model checking, game theory, and executable models based on logic programming.

**Internet/Network Security**

*UC Berkeley*

- This class aims to provide a thorough grounding in network security suitable for those interested in conducting research in the area, as well as students more generally interested in either security or networking. We will also look at broader issues relating to Internet security for which networking plays a role. Topics include: denial-of-service; capabilities; network intrusion detection; worms; forensics; scanning; traffic analysis / inferring activity; architecture; protocol issues; legality and ethics; web attacks; anonymity; honeypots; botnets; spam; the underground economy; research pitfalls. The course is taught with an emphasis on seminal papers rather than bleeding-edge for a given topic.

**Modern Binary Exploitation**

*Rensselaer Polytechnic Institute*

- This repository contains the materials as developed and used by RPISEC to

## Courses

### Artificial Intelligence

**The Society of Mind**

*MIT*

- This course is an introduction, by Prof. Marvin Minsky, to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.
- Lectures
- Assignments
- Readings

## Courses

### Computer Graphics

**Graduate Computer Graphics**

*New York University*

- Step-by-step study computer graphics, with reading and homework at each lecture (Fall2015)
- Lectures

## Courses

### Misc

**Computer Networks**

*UC Berkeley*

- This is an undergraduate level course covering the fundamental concepts of networking as embodied in the Internet. The course will cover a wide range of topics; see the lecture schedule for more details. While the class has a textbook, we will not follow its order of presentation but will instead use the text as a reference when covering each individual topic. The course will also have several projects that involve programming (in Python).
- You should know programming, data structures, and software engineering. In terms of mathematics, your algebra should be very solid, you need to know basic probability, and you should be comfortable with thinking abstractly. The TAs will spend very little time reviewing material that is not specific to networking. We assume that you either know the material covered in those courses, or are willing to learn the material as necessary. We won't cover any of this material in lecture.

**Advanced Topics in Computer Systems**

*UC Berkeley*

- CS262a is the first semester of a year-long sequence on computer systems research, including operating systems, database systems, and Internet infrastructure systems. The goal of the course is to cover a broad array of research topics in computer systems, and to engage you in top-flight systems research. The first semester is devoted to basic thematic issues and underlying techniques in computer systems, while the second semester goes deeper into topics related to scalable, parallel and distributed systems. The class is based on a discussion of important research papers and a research project.
**Parts**: Some Classics, Persistent Storage, Concurrency, Higher-Level Models, Virtual Machines, Cloud Computing, Parallel and Distributed Computing, Potpourri.- Prerequisites: The historical prerequisite was to pass an entrance exam in class, which covered undergraduate operating systems material (similar to UCB's CS162). There is no longer an exam. However, if you have not already taken a decent undergrad OS class, you should talk with me before taking this class. The exam had the benefit of "paging in" the undergrad material, which may have been its primary value (since the pass rate was high).
- Readings & Lectures

**Cutting-edge Web Technologies**

*Berkeley*

- Want to learn what makes future web technologies tick? Join us for the class where we will dive into the internals of many of the newest web technologies, analyze and dissect them. We will conduct survey lectures to provide the background and overview of the area as well as invite guest lecturers from various leading projects to present their technologies.

## Sep 26th - Oct 2nd, 2016

## Courses

### Computer Graphics

**Computer Graphics**

*Carnegie Mellon University*

- This course provides a comprehensive introduction to computer graphics. Focuses on fundamental concepts and techniques, and their cross-cutting relationship to multiple problem domains in graphics (rendering, animation, geometry, imaging). Topics include: sampling, aliasing, interpolation, rasterization, geometric transformations, parameterization, visibility, compositing, filtering, convolution, curves & surfaces, geometric data structures, subdivision, meshing, spatial hierarchies, ray tracing, radiometry, reflectance, light fields, geometric optics, Monte Carlo rendering, importance sampling, camera models, high-performance ray tracing, differential equations, time integration, numerical differentiation, physically-based animation, optimization, numerical linear algebra, inverse kinematics, Fourier methods, data fitting, example-based synthesis.
- Lectures and Readings
- Assignments and Quizes

## Courses

### Machine Learning

**Algorithms for Big Data**

*Harvard University*

- Big data is data so large that it does not fit in the main memory of a single machine, and the need to process big data by efficient algorithms arises in Internet search, network traffic monitoring, machine learning, scientific computing, signal processing, and several other areas. This course will cover mathematically rigorous models for developing such algorithms, as well as some provable limitations of algorithms operating in those models.
- Lectures (Youtube)
- Assignments

## Sep 12th - Sep 18th, 2016

## Courses

### Misc

**Monte Carlo Methods and Stochastic Optimization**

*Harvard University*

- This course introduces important principles of Monte Carlo techniques and demonstrates the power of these techniques with simple (but very useful) applications. All of this in Python!
- Lecture Videos
- Assignments
- Lecture Notes

## Jul 4th - Jul 10th, 2016

## Courses

### Systems

**Database Systems**

*Carnegie-Mellon University*

- This course is a comprehensive study of the internals of modern database management systems. It will cover the core concepts and fundamentals of the components that are used in both high-performance transaction processing systems (OLTP) and large-scale analytical systems (OLAP). The class will stress both efficiency and correctness of the implementation of these ideas. All class projects will be in the context of a real in-memory, multi-core database system. The course is appropriate for graduate students in software systems and for advanced undergraduates with strong systems programming skills.
- Assignments
- Lecture Videos
- Readings

**Engineering Distributed Systems**

*Carnegie-Mellon University*

- A project focused course on Distributed Systems with an awesome list of readings
- Readings

## Jun 13th - Jun 19th, 2016

## Courses

### Systems

**GPU Programming**

*Caltech*

- This course will cover programming techniques for the GPU. The course will introduce NVIDIA's parallel computing language, CUDA. Beyond covering the CUDA programming model and syntax, the course will also discuss GPU architecture, high performance computing on GPUs, parallel algorithms, CUDA libraries, and applications of GPU computing.
- Assignments
- Lecture Notes

## May 23rd - May 29th, 2016

## Courses

### Systems

**Introduction to Computer Systems (ICS)**

*Carnegie-Mellon University*

- The ICS course provides a programmer's view of how computer systems execute programs, store information, and communicate. It enables students to become more effective programmers, especially in dealing with issues of performance, portability and robustness. It also serves as a foundation for courses on compilers, networks, operating systems, and computer architecture, where a deeper understanding of systems-level issues is required. Topics covered include: machine-level code and its generation by optimizing compilers, performance evaluation and optimization, computer arithmetic, memory organization and management, networking technology and protocols, and supporting concurrent computation.
- This is the must-have course for everyone in CMU who wants to learn some computer science no matter what major are you in. Because it's CMU (The course number is as same as the zip code of CMU)!
- Lecture Notes
- Videos
- Assignments

## Courses

### Misc

**Android App Development, Spring 2016**

*Stanford University*

- Course Description: This course provides an introduction to developing applications for the Android mobile platform.
- Prerequisite: CS 106B or equivalent. Java experience highly recommended. OOP highly recommmended.
- Devices: Access to an Android phone and/or tablet recommended but not required.
- Videos: Videos list can be found here
- Other materials: Some codes, handsout, homework ..... and lecture notes are not downloadable on the site due to login requirement. Please head to my Github repo herestars110 to download them.

## Mar 14th - Mar 20th, 2016

## Courses

### Programming Languages / Compilers

**Rust Programming**

*UPenn*

- This course covers what makes Rust so unique and applies it to practical systems programming problems. Topics covered include traits and generics; memory safety (move semantics, borrowing, and lifetimes); Rust’s rich macro system; closures; and concurrency.
- Assignmentsstars173

## Courses

### Misc

**Realistic Image Synthesis**

*Cornell University*

- CS6630 is an introduction to physics-based rendering at the graduate level. Starting from the fundamentals of light transport we will look at formulations of the Rendering Equation, and a series of Monte Carlo methods, from sequential sampling to multiple importance sampling to Markov Chains, for solving the equation to make pictures. We'll look at light reflection from surfaces and scattering in volumes, illumination from luminaries and environments, and diffusion models for translucent materials. We will build working implementations of many of the algorithms we study, and learn how to make sure they are actually working correctly. It's fun to watch integrals and probability distributions transform into photographs of a slightly too perfect synthetic world.
- Syllabus
- Lectures
- Assignments
- Readings

**Computational Photography**

*Cornell University*

- A course on the emerging applications of computation in photography. Likely topics include digital photography, unconventional cameras and optics, light field cameras, image processing for photography, techniques for combining multiple images, advanced image editing algorithms, and projector-camera systems.cornell.edu/courses/CS6630/2012sp/about.stm)
- Lectures
- Assignments

## Mar 7th - Mar 13th, 2016

## Courses

### Computer Graphics

**3D Reconstruction with Computer Vision**

*UTexas*

- In this lab-based class, we'll dive into practical applications of 3D reconstruction, combining hardware and software to build our own 3D environments from scratch. We'll use open-source frameworks like OpenCV to do the heavy lifting, with the focus on understanding and applying state-of-the art approaches to geometric computer vision
- Lectures

**Introduction to Computer Graphics**

*Cornell University*

- The study of creating, manipulating, and using visual images in the computer.
- Assignments
- Exams

**Introduction to Computer Vision**

*Cornell University*

- This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
- Assignments
- Lectures

**Computer Vision**

*Cornell University*

- Introduction to computer vision. Topics include edge detection, image segmentation, stereopsis, motion and optical flow, image mosaics, 3D shape reconstruction, and object recognition. Students are required to implement several of the algorithms covered in the course and complete a final project.
- Syllabus
- Lectures
- Assignments

## Courses

### Artificial Intelligence

**Introduction to Artificial Intelligence**

*UC Berkeley*

- This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
- Lectures
- Projects
- Exams

**Foundations of Artificial Intelligence**

*Cornell University*

- This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
- Assignments
- Lectures

**Advanced Artificial Intelligence**

*Cornell University*

## Feb 22nd - Feb 28th, 2016

## Courses

### Machine Learning

**Learning from Data**

*Caltech*

- This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.
- Lectures
- Homework
- Textbook

## Feb 15th - Feb 21st, 2016

## Courses

### Machine Learning

**Convolutional Neural Networks for Visual Recognition**

*Stanford University*

- Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.
- Lecture Notes
- Lecture Videos
- Github Page

## Jan 11th - Jan 17th, 2016

## Courses

### Machine Learning

**Data Science**

*Harvard University*

- Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.
- Lectures
- Slides
- Labs and Assignments
- 2014 Lectures
- 2013 Lectures
*(slightly better)*

## Oct 26th - Nov 1st, 2015

## Courses

### Introduction to CS

**Structure and Interpretation of Computer Programs [Python]**

*UC Berkeley*

- In CS 61A, we are interested in teaching you about programming, not about how to use one particular programming language. We consider a series of techniques for controlling program complexity, such as functional programming, data abstraction, and object-oriented programming. Mastery of a particular programming language is a very useful side effect of studying these general techniques. However, our hope is that once you have learned the essence of programming, you will find that picking up a new programming language is but a few days' work.
- Lecture Resources by Type
- Lecture Resources by Topic
- Additional Resources
- Practice Problems
- Extra Lectures

**Structure & Interpretation of Computer Programs [Racket]**

*UC Berkeley*

- A self-paced version of the CS61 Course but in Racket / Scheme. 61AS is a great introductory course that will ease you into all the amazing concepts that future CS courses will cover, so remember to keep an open mind, have fun, and always respect the data abstraction
- Lecture Videos
- Assignments and Notes

## Courses

### Machine Learning

**Advanced Robotics**

*UC Berkeley*

- The course introduces the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization---two areas with wide applicability in modern Artificial Intelligence. An intended side-effect of the course is to generally strengthen your expertise in these two areas.
- Lectures Notes
- Assignments

## Oct 19th - Oct 25th, 2015

## Courses

### Security

**Offensive Computer Security**

*Florida State University*

- Course taught by W. Owen Redwood and Xiuwen Liu. It covers a wide range of computer security topics, starting from Secure C Coding and Reverse Engineering to Penetration Testing, Exploitation and Web Application Hacking, both from the defensive and the offensive point of view.
- Lectures and Videos
- Assignments

**System Security**

*Cornell University*

- This course discusses security for computers and networked information systems. We focus on abstractions, principles, and defenses for implementing military as well as commercial-grade secure systems.
- Syllabus
- Lectures
- Assignments

## Oct 12th - Oct 18th, 2015

## Courses

### Systems

**Introduction to the Internet: Architecture and Protocols**

*UC Berkeley*

- This course is an introduction to the Internet architecture. We will focus on the concepts and fundamental design principles that have contributed to the Internet's scalability and robustness and survey the various protocols and algorithms used within this architecture. Topics include layering, addressing, intradomain routing, interdomain routing, reliable delivery, congestion control, and the core protocols (e.g., TCP, UDP, IP, DNS, and HTTP) and network technologies (e.g., Ethernet, wireless).
- Lecture Notes & Assignments
- Discussion Notes

## Jul 13th - Jul 19th, 2015

## Courses

### Introduction to CS

**Programming Methodology**

*Stanford University*

- This course is the largest of the introductory programming courses and is one of the largest courses at Stanford. Topics focus on the introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Programming Methodology teaches the widely-used Java programming language along with good software engineering principles.
- Lecture Videos
- Assignments
- All materials in a zip file

**Programming Abstractions**

*Stanford University*

- This course is the natural successor to Programming Methodology and covers such advanced programming topics as recursion, algorithmic analysis, and data abstraction using the C++ programming language, which is similar to both C and Java.
- Lectures
- Assignments
- All materials in a zip file

**Programming Paradigms**

*Stanford University*

- Topics: Advanced memory management features of C and C++; the differences between imperative and object-oriented paradigms. The functional paradigm (using LISP) and concurrent programming (using C and C++)
- Lectures
- Assignments

## May 18th - May 24th, 2015

## Courses

### Misc

**An Introduction to Efficient Scientific Computation**

*Universität Bremen*

- This is a graduate course in scientific computing created and taught by Oliver Serang in 2014, which covers topics in computer science and statistics with applications from biology. The course is designed top-down, starting with a problem and then deriving a variety of solutions from scratch.
- Topics include memoization, recurrence closed forms, string matching (sorting, hash tables, radix tries, and suffix tries), dynamic programming (e.g. Smith-Waterman and Needleman-Wunsch), Bayesian statistics (e.g. the envelope paradox), graphical models (HMMs, Viterbi, junction tree, belief propagation), FFT, and the probabilistic convolution tree.
- Lecture videos on Youtube and for direct download

## May 4th - May 10th, 2015

## Courses

### Machine Learning

**Deep Learning for Natural Language Processing**

*Stanford University*

- Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate most everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc. There are a large variety of underlying tasks and machine learning models powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks. These models can often be trained with a single end-to-end model and do not require traditional, task-specific feature engineering. In this spring quarter course students will learn to implement, train, debug, visualize and invent their own neural network models. The course provides a deep excursion into cutting-edge research in deep learning applied to NLP.
- Syllabus
- Lectures and Assignments

## Apr 6th - Apr 12th, 2015

## Courses

### CS Theory

**Great Ideas in Theoretical Computer Science**

*MIT*

- This course provides a challenging introduction to some of the central ideas of theoretical computer science. Beginning in antiquity, the course will progress through finite automata, circuits and decision trees, Turing machines and computability, efficient algorithms and reducibility, the P versus NP problem, NP-completeness, the power of randomness, cryptography and one-way functions, computational learning theory, and quantum computing. It examines the classes of problems that can and cannot be solved by various kinds of machines. It tries to explain the key differences between computational models that affect their power.
- Syllabus
- Lecture Notes
- Lecture Videos

## Mar 16th - Mar 22nd, 2015

## Courses

### Systems

**Great Ideas in Computer Architecture (Machine Structures)**

*UC Berkeley*

- The subjects covered in this course include: C and assembly language programming, translation of high-level programs into machine language, computer organization, caches, performance measurement, parallelism, CPU design, warehouse-scale computing, and related topics.
- Lecture Videos
- Lecture Notes
- Resources
- Old Exams

## Mar 2nd - Mar 8th, 2015

## Courses

### Computer Graphics

**Computer Vision**

*University of Central Florida*

- An introductory level course covering the basic topics of computer vision, and introducing some fundamental approaches for computer vision research.
- Lectures and Videos
- Assignments

## Feb 9th - Feb 15th, 2015

## Courses

### Systems

**Unix Tools & Scripting**

*Cornell University*

- UNIX-like systems are increasingly being used on personal computers, mobile phones, web servers, and many other systems. They represent a wonderful family of programming environments useful both to computer scientists and to people in many other fields, such as computational biology and computational linguistics, in which data is naturally represented by strings. This course provides an intensive training to develop skills in Unix command line tools and scripting that enable the accomplishment and automation of large and challenging computing tasks. The syllabus takes students from shell basics and piping, to regular-expression processing tools, to shell scripting and Python.
- Syllabus
- Lectures
- Assignments

**Operating Systems**

*Cornell University*

- CS 4410 covers systems programming and introductory operating system design and implementation. We will cover the basics of operating systems, namely structure, concurrency, scheduling, synchronization, memory management, filesystems, security and networking. The course is open to any undergraduate who has mastered the material in CS3410/ECE3140.
- Syllabus
- Lectures

## Courses

### CS Theory

**Data Structures and Functional Programming**

*Cornell University*

- CS 3110 (formerly CS 312) is the third programming course in the Computer Science curriculum, following CS 1110/1112 and CS 2110. The goal of the course is to help students become excellent programmers and software designers who can design and implement software that is elegant, efficient, and correct, and whose code can be maintained and reused.
- Syllabus
- Lectures
- Assignments

**Introduction to Theory of Computing**

*Cornell University*

- This undergraduate course provides a broad introduction to the mathematical foundations of computer science. We will examine basic computational models, especially Turing machines. The goal is to understand what problems can or cannot be solved in these models.
- Syllabus
- Lectures
- Assignments

## Courses

### Introduction to CS

**Fundamental Programming Concepts**

*Cornell University*

- This course provides an introduction to programming and problem solving using a high-level programming language. It is designed to increase your knowledge level to comfortably continue to courses CS111x. Our focus will be on generic programming concepts: variables, expressions, control structures, loops, arrays, functions, pseudocode and algorithms. You will learn how to analyze problems and convert your ideas into solutions interpretable by computers. We will use MATLAB; because it provides a productive environment, and it is widely used by all engineering communities.
- Syllabus
- Lectures
- Assignments

**Introduction to Computing Using Python**

*Cornell University*

- Programming and problem solving using Python. Emphasizes principles of software development, style, and testing. Topics include procedures and functions, iteration, recursion, arrays and vectors, strings, an operational model of procedure and function calls, algorithms, exceptions, object-oriented programming, and GUIs (graphical user interfaces). Weekly labs provide guided practice on the computer, with staff present to help. Assignments use graphics and GUIs to help develop fluency and understanding.
- Syllabus
- Lectures
- Assignments

**Introduction to Computing Using Matlab**

*Cornell University*

- Programming and problem solving using MATLAB. Emphasizes the systematic development of algorithms and programs. Topics include iteration, functions, arrays and vectors, strings, recursion, algorithms, object-oriented programming, and MATLAB graphics. Assignments are designed to build an appreciation for complexity, dimension, fuzzy data, inexact arithmetic, randomness, simulation, and the role of approximation. NO programming experience is necessary; some knowledge of Calculus is required.
- Syllabus
- Lectures
- Assignments
- Projects

**Introduction to Computational Science and Engineering Using Matlab Graphical User Interfaces**

*Cornell University*

- Programming and problem solving using MATLAB. Emphasizes the systematic development of algorithms and programs. Topics include iteration, functions, arrays and vectors, strings, recursion, algorithms, object-oriented programming, and MATLAB graphics. Assignments are designed to build an appreciation for complexity, dimension, fuzzy data, inexact arithmetic, randomness, simulation, and the role of approximation. NO programming experience is necessary; some knowledge of Calculus is required.
- Syllabus
- Lectures
- Projects

**Transition to OO Programming**

*Cornell University*

- Introduction to object-oriented concepts using Java. Assumes programming knowledge in a language like MATLAB, C, C++, or Fortran. Students who have learned Java but were not exposed heavily to OO programming are welcome.
- Syllabus
- Lectures
- Assignments

**Transition to Python**

*Cornell University*

- Introduction to the Python programming language. Covers the basic programming constructs of Python, including assignment, conditionals, iteration, functions, object-oriented design, arrays, and vectorized computation. Assumes programming knowledge in a language like Java, Matlab, C, C++, or Fortran.
- Syllabus
- Lectures
- Assignments

**Object-Oriented Programming and Data Structures**

*Cornell University*

- CS 2110 is an intermediate-level programming course and an introduction to computer science. Topics include program design and development, debugging and testing, object-oriented programming, proofs of correctness, complexity analysis, recursion, commonly used data structures, graph algorithms, and abstract data types. Java is the principal programming language. The course syllabus can easily be extracted by looking at the link to lectures.
- Syllabus
- Lectures
- Assignments

**Web Information Systems**

*Cornell University*

- This course will introduce you to technologies for building data-centric information systems on the World Wide Web, show the practical applications of such systems, and discuss their design and their social and policy context by examining cross-cutting issues such as citizen science, data journalism and open government. Course work involves lectures and readings as well as weekly homework assignments, and a semester-long project in which the students demonstrate their expertise in building data-centric Web information systems.
- Syllabus
- Lectures
- Assignments

## Courses

### Misc

**Introduction to Computer Game Development**

*Cornell University*

- A project-based course in which programmers and designers collaborate to make a computer game. This course investigates the theory and practice of developing computer games from a blend of technical, aesthetic, and cultural perspectives. Technical aspects of game architecture include software engineering, artificial intelligence, game physics, computer graphics, and networking. Aesthetic and cultural include art and modeling, sound and music, game balance, and player experience.
- Syllabus
- Lectures
- Assignments

**Advanced Topics in Computer Game Development**

*Cornell University*

- Project-based follow-up course to CS/INFO 3152. Students work in a multidisciplinary team to develop a game that incorporates innovative game technology. Advanced topics include 3D game development, mobile platforms, multiplayer gaming, and nontraditional input devices. There is a special emphasis on developing games that can be submitted to festivals and competitions, or that can be commercialized.
- Syllabus
- Lectures
- Assignments

**Analytics-driven Game Design**

*Cornell University*

- A project-based course in which programmers and designers collaborate to design, implement, and release a video game online through popular game portals. In this course, students will use the internet to gather data anonymously from players. Students will analyze this data in order to improve their game over multiple iterations. Technical aspects of this course include programming, database architecture, and statistical analysis.
- Syllabus
- Lectures
- Assignments

**Applied Logic**

*Cornell University*

- In addition to basic first-order logic, when taught by Computer Science this course involves elements of Formal Methods and Automated Reasoning. Formal Methods is concerned with proving properties of algorithms, specifying programming tasks and synthesizing programs from proofs. We will use formal methods tools such as interactive proof assistants (see www.nuprl.org). We will also spend two weeks on constructive type theory, the language used by the Coq and Nuprl proof assistants.
- Syllabus
- Lectures
- Assignments

**Applications of Parallel Computers**

*Cornell University*

- How do we solve the large-scale problems of science quickly on modern computers? How do we measure the performance of new or existing simulation codes, and what things can we do to make them run faster? How can we best take advantage of features like multicore processors, vector units, and graphics co-processors? These are the types of questions we will address in CS 5220, Applications of Parallel Computers. Topics include:
- Single-processor architecture, caches, and serial performance tuning
- Basics of parallel machine organization
- Distributed memory programming with MPI
- Shared memory programming with OpenMP
- Parallel patterns: data partitioning, synchronization, and load balancing
- Examples of parallel numerical algorithms
- Applications from science and engineering

- Lectures
- Assignments

**Computational Techniques for Analyzing Clinical Data**

*Cornell University*

- CS5540 is a masters-level course that covers a wide range of clinical problems and their associated computational challenges. The practice of medicine is filled with digitally accessible information about patients, ranging from EKG readings to MRI images to electronic health records. This poses a huge opportunity for computer tools that make sense out of this data. Computation tools can be used to answer seemingly straightforward questions about a single patient's test results (“Does this patient have a normal heart rhythm?”), or to address vital questions about large populations (“Is there any clinical condition that affects the risks of Alzheimer”). In CS5540 we will look at many of the most important sources of clinical data and discuss the basic computational techniques used for their analysis, ranging in sophistication from current clinical practice to state-of-the-art research projects.
- Syllabus
- Lectures
- Assignments

**Evolutionary Computation**

*Cornell University*

- This course will cover advanced topics in evolutionary algorithms and their application to open-ended computational design. The field of evolutionary computation tries to address large-scale optimization and planning problems through stochastic population-based methods. It draws inspiration from evolutionary processes in nature and in engineering, and also serves as abstract models for these phenomena. Evolutionary processes are generally weak methods that require little information about the problem domain and hence can be applied across a wide variety of applications. They are especially useful for open-ended problem domains for which little formal knowledge exists and the number of parameters is undefined, such as for the general engineering design process. This course will provide insight to a variety of evolutionary computation paradigms, such as genetic algorithms, genetic programming, and evolutionary strategies, as well as governing dynamics of co-evolution, arms races and mediocre stable states. New methods involving symbiosis models and pattern recognition will also be presented. The material will be intertwined with discussions of representations and results for design problems in a variety of problem domains including software, electronics, and mechanics.
- Syllabus
- Lectures
- Assignments

**Computational Motion**

*Cornell University*

- Covers computational aspects of motion, broadly construed. Topics include the computer representation, modeling, analysis, and simulation of motion, and its relationship to various areas, including computational geometry, mesh generation, physical simulation, computer animation, robotics, biology, computer vision, acoustics, and spatio-temporal databases. Students implement several of the algorithms covered in the course and complete a final project. This offering will also explore the special role of motion processing in physically based sound rendering.

**Algorithmic Game Theory**

*Cornell University*

- Algorithmic Game Theory combines algorithmic thinking with game-theoretic, or, more generally, economic concepts. The course will study a range of topics at this interface
- Syllabus
- Lectures
- Assignments
- Readings

## Feb 2nd - Feb 8th, 2015

## Courses

### Systems

**Operating Systems**

*University of Arkansas (Fayetteville)*- An introduction to operating systems including topics in system structures, process management, storage management, files, distributed systems, and case studies.

**Computer Architecture 3**

*CUNY Hunter College*

- A course that covers cache design, buses, memory hierarchies, processor-peripheral interfaces, and multiprocessors, including GPUs.

## Courses

### Algorithms

**Data Structures and Object Oriented Design**

*University of Southern California (USC)*

## Jan 26th - Feb 1st, 2015

## Introduction

### Legend

## Courses

### Systems

**Operating Systems**

*Stanford University*

- This class introduces the basic facilities provided in modern operating systems. The course divides into three major sections. The first part of the course discusses concurrency. The second part of the course addresses the problem of memory management. The third major part of the course concerns file systems.
- Lecture Notes
- Assignments

**Operating Systems and Systems Programming**

*UC Berkeley*

- The purpose of this course is to teach the design of operating systems and operating systems concepts that appear in other advanced systems. Topics we will cover include concepts of operating systems, systems programming, networked and distributed systems, and storage systems, including multiple-program systems (processes, interprocess communication, and synchronization), memory allocation (segmentation, paging), resource allocation and scheduling, file systems, basic networking (sockets, layering, APIs, reliability), transactions, security, and privacy.
- Operating Systems course by the Chair of EECS, UC Berkeley David Culler
- Lecture Videos Spring 2015 lectures
- Lecture Notes Spring 2015 lectures

**Real-Time Programming**

*University of Waterloo*

- Write a real-time OS microkernel in C, and application code to operate a model train set in response to real-time sensor information. The communication with the train set runs at 2400 baud so it takes about 61 milliseconds to ask all of the sensors for data about the train's possible location. This makes it particularly challenging because a train can move about 3 centimeters in that time. One of the most challenging and time-consuming courses at the University of Waterloo.
- Assignments
- Lecture notes

**Operating Systems**

*University of Virginia*

- A course (that) covers topics including: Analysis process communication and synchronization; resource management; virtual memory management algorithms; file systems; and networking and distributed systems. The primary goal of this course is to improve your ability to build scalable, robust and secure computing systems. It focuses on doing that by understanding what underlies the core abstractions of modern computer systems.
- Syllabus
- Lectures

**Principles of Distributed Computing**

*ETH-Zurich*

- Explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing in an easy-to-read set of lecture notes, combined with complete exercises and solutions.
- Book
- Assignments and Solutions

**Parallelism and Concurrency**

*Univ of Washington*

- Technically not a course nevertheless an awesome collection of materials used by Prof Dan Grossman to teach parallelism and concurrency concepts to sophomores at UWash

**Distributed Systems**

*MIT*

- MIT's graduate-level DS course with a focus on fault tolerance, replication, and consistency, all taught via awesome lab assignments in Golang!
- Assignments - Just do
`git clone git://g.csail.mit.edu/6.824-golabs-2014 6.824`

- Readings

**Operating Systems**

*MIT*

- MIT's operating systems course focusing on the fundamentals of OS design including booting, memory management, environments, file systems, multitasking, and more. In a series of lab assignments, you will build JOS, an OS exokernel written in C.
- Assignments
- Lectures
- Videos Note: These are student recorded cam videos of the 2011 course. The videos explain a lot of concepts required for the labs and assignments.

**Distributed Systems**

*Carnegie-Mellon University*

- Introduction to distributed systems with a focus on teaching concepts via projects implemented in the Go programming language.
- Assignments

## Courses

### Programming Languages / Compilers

**Introduction to Haskell**

*Penn Engineering*

- Explore the joys of functional programming, using Haskell as a vehicle. The aim of the course will be to allow you to use Haskell to easily and conveniently write practical programs.
- Previous semester also available, with more exercises

**Functional Programming with Clojure**

*University of Helsinki*

- The course is an introduction to functional programming with a dynamically typed language Clojure. We start with an introduction to Clojure; its syntax and development environment. Clojure has a good selection of data structures and we cover most of them. We also go through the basics of recursion and higher-order functions. The course material is in English.
- Github Page

**Functional Programming**

*Princeton University*

- Covers functional programming concepts like closures, tail-call recursion & parallelism using the OCaml programming language
- Lectures
- Assignments

**Hack your language!**

*UC Berkeley*

- Introduction to programming languages by designing and implementing domain-specific languages.
- Lecture Videos
- Code for Assignments

**Programming Languages**

*Brown University*

- Course by Prof. Krishnamurthi (author of HtDP) and numerous other awesome books on programming languages. Uses a custom designed Pyret programming language to teach the concepts. There was an online class hosted in 2012, which includes all lecture videos for you to enjoy.
- Videos
- Assignments

**Functional Systems in Haskell**

*Stanford University*

- Building software systems in Haskell
- Lecture Slides
- 3 Assignments: Lab1, Lab2, Lab3

**Programming Languages and Compilers**

*Univ of Illinois, Urbana-Champaign*

## Courses

### Algorithms

**Fundamental Algorithms**

*Univ of Illinois, Urbana-Champaign*

- Algorithms class covering recursion, randomization, amortization, graph algorithms, network flows and hardness. The lecture notes by Prof. Erikson are comprehensive enough to be a book by themselves. Highly recommended!
- Lecture Notes
- Labs and Exams

**Program & Data Representation**

*University of Virginia*

- This data structures course introduces C++, linked-lists, stacks, queues, trees, numerical representation, hash tables, priority queues, heaps, huffman coding, graphs, and x86 assembly.
- Lectures
- Assignments

**Software Design and Analysis I**

## Courses

### CS Theory

**Software Foundations**

*University of Pennsylvania*

- An introduction to formal verification of software using the Coq proof assistant. Topics include basic concepts of logic, computer-assisted theorem proving, functional programming, operational semantics, Hoare logic, and static type systems.
- Lectures and Assignments
- Textbook

**Mathematical Foundations of Computing**

*Stanford University*

- CS103 is a first course in discrete math, computability theory, and complexity theory. In this course, we'll probe the limits of computer power, explore why some problems are harder to solve than others, and see how to reason with mathematical certainty.
- Links to all lectures notes and assignments are directly on the course page

**Discrete Structures**

*Univ of Illinois Urbana-Champaign*

- This course is an introduction to the theoretical side of computer science. In it, you will learn how to construct proofs, read and write literate formal mathematics, get a quick introduction to key theory topics and become familiar with a range of standard mathematics concepts commonly used in computer science.
- Textbook Written by the professor. Includes Instructor's Guide.
- Assignments
- Exams

**Foundations of Cryptography**

*UC Berkeley*

- This course discusses the complexity-theory foundations of modern cryptography, and looks at recent results in the field such as Fully Homomorphic Encryption, Indistinguishability Obfuscation, MPC and so on.

**Complexity Theory**

*UC Berkeley*

- A graduate level course on complexity theory that introduces P vs NP, the power of randomness, average-case complexity, hardness of approximation, and so on.

**Algorithms & Models of Computation (Fall 2014)**

*University of Illinois Urbana-Champaign*

- CS 498 section 374 (unofficially "CS 374") covers fundamental tools and techniques from theoretical computer science, including design and analysis of algorithms, formal languages and automata, computability, and complexity. Specific topics include regular and context-free languages, finite-state automata, recursive algorithms (including divide and conquer, backtracking, dynamic programming, and greedy algorithms), fundamental graph algorithms (including depth- and breadth-first search, topological sorting, minimum spanning trees, and shortest paths), undecidability, and NP-completeness. The course also has a strong focus on clear technical communication.
- Assignments/Exams
- Lecture Notes/Labs
- Lecture videos

**Programming Paradigms**

*University of Arkansas (Fayetteville)*

- Programming in different paradigms with emphasis on object oriented programming, network programming and functional programming. Survey of programming languages, event driven programming, concurrency, software validation.
- Syllabus
- Notes
- Assignments
- Practice Exams

## Courses

### Introduction to CS

**The Beauty and Joy of Computing**

*UC Berkeley*

- CS10 is UCB's introductory computer science class, taught using the beginners' drag-and-drop language. Students learn about history, social implications, great principles, and future of computing. They also learn the joy of programming a computer using a friendly, graphical language, and will complete a substantial team programming project related to their interests.
- Snap*!* (based on Scratch by MIT).
- Curriculum

**Introduction to Computer Science**

*Harvard University*

- CS50x is Harvard College's introduction to the intellectual enterprises of computer science and the art of programming f