• • Awesome Courses：名校计算机科学课程清单。从各个名校的网站主页抓取的计算机科学课程清单，分为6大部分：系统；编程语言/编译器；算法；CS理论；CS导论；杂项等等。值得关注的趋势是编程语言中函数式课程比例大幅度上升！

    Table of Contents

 

Misc

 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

 Software Engineering Cornell University

• Introduction to the practical problems of specifying, designing, building, testing, and delivering reliable software systems

 Machine Learning Carnegie Mellon University

• Taught by one of the leading experts on Machine Learning - Tom Mitchell

 GIS & Spatial Analysis UC Santa Barbara

• Taught by , , and 
• Focuses on specific computational languages (e.g., Python, R, shell) and tools (e.g., GDAL/OGR, InVEST, MGET, ModelBuilder) applied to the spatial analysis of environmental problems
• (includes lecture materials and labs)

 Web Programming University of Washington

• This course is an introduction to programming for the World Wide Web. Covers use of HTML, CSS, PHP, JavaScript, AJAX, and SQL.

 Humanitarian Free & Open Source Software Development Rochester Institute of Technology

• This course provides students with exposure to the design, creation and production of Open Source Software projects. Students will be introduced to the historic intersections of technology and intellectual property rights and will become familiar with Open Source development processes, tools and practices.

 Advanced Project Based Free & Open Source Software DevelopmentRochester Institute of Technology

• A project based course where students develop 3 tightly scoped open source projects for the Raspberry Pi.

 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.

 Computer Vision and Computational Photography University of Pennsylvania

• An introductory course in computer vision and computational photography focusing on four topics: image features, image morphing, shape matching, and image search.

 Computer & Network Security University of Michigan

• Taught by  who has analyzed the security of Electronic Voting Machines in the  and  .
• This intensive research seminar covers foundational work and current topics in computer systems security.

 Software Engineering University of Hawaii

• Taught by 
• Introduction to software engineering using the 

 Machine Learning Columbia University

• Course taught by  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.

 Introduction to Game Development Tufts University

• The course taught by  teaches game development initially in PyGame through Python, before moving on to addressing all facets of game development. Topics addressed include game pysics, sprites, animation, game development methodology, sound, testing, MMORPGs and online games, and addressing mobile development in Android, HTML5, and iOS. Most to all of the development is focused on PyGame for learning principles

 Offensive Computer Security Florida State University

• Course taught by  and . 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.

 Deep Learning Carnegie Mellon University

• The course presents the subject through a series of seminars and labs, which will explore it from its early beginnings, and work themselves to some of the state of the art. The seminars will cover the basics of deep learning and the underlying theory, as well as the breadth of application areas to which it has been applied, as well as the latest issues on learning from very large amounts of data. We will concentrate largely, although not entirely, on the connectionist architectures that are most commonly associated with it. Lectures and Reading Notes are available on the page.

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.
•  (based on Scratch by MIT).

 Introduction to Computer Science Harvard University

• CS50x is Harvard College's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan.

 Computer Science 101 Stanford University

• CS101 teaches the essential ideas of Computer Science for a zero-prior-experience audience. Participants play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers.
• Lectures videos will available for free after registration.

 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.

 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.

 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++)

 Structure and Interpretation of Computer Programs MIT

• Teaches big-picture computing concepts using the Scheme programming language. Students will implement programs in a variety of different programming paradigms (functional, object-oriented, logical). Heavy emphasis on function composition, code-as-data, control abstraction with continuations, and syntactic abstraction through macros. An excellent course if you are looking to build a mental framework on which to hang your programming knowledge.
•  (, )

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.

 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

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

 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.
•  Written by the professor. Includes Instructor's Guide.

Algorithms

 Hacking a Google Interview MIT

• This course taught in the MIT Independent Activities Period in 2009 goes over common solution to common interview questions for software engineer interviews at highly selective companies like Apple, Google, and Facebook. They cover time complexity, hash tables, binary search trees, and other common algorithm topics you should have already covered in a different course, but goes more in depth on things you wouldn't otherwise learn in class- like bitwise logic and problem solving tricks.

 Analysis of Algorithms Stony Brook University

• Prof Steven Skiena's no stranger to any student when it comes to algorithms. His seminal  has been touted by many to be best for . In addition, he's also well-known for tutoring students in competitive . If you're looking to brush up your knowledge on Algorithms, you can't go wrong with this course.

 Algorithms Carnegie Mellon University

• The required algorithms class that go in depth into all basic algorithms and the proofs behind them. This is one of the heavier algorithms curriculums on this page. Taught by Avrim Blum and Manuel Blum who has a Turing Award due to his contributions to algorithms. Course link includes a very comprehensive set of reference notes by Avrim Blum.

 Data Structures and Algorithms Princeton University

• The  algorithms class covering most important algorithms and data structures in use on computers taught by Robert Sedgewick.

 Data Structures 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.

 Software Design and Analysis I CUNY Hunter College

• It is currently an intensive introduction to program development and problem solving. Its emphasis is on the process of designing, implementing, and evaluating small-scale programs. It is not supposed to be a C++ programming course, although much of the course is spent on the details of C++. C++ is an extremely large and complex programming language with many features that interact in unexpected ways. One does not need to know even half of the language to use it well.

-  Software Design and Analysis II CUNY Hunter College

• Introduces algorithms for a few common problems such as sorting. Practically speaking, it furthers the students' programming skills with topics such as recursion, pointers, and exception handling, and provides a chance to improve software engineering skills and to give the students practical experience for more productive programming.

 Software Design and Analysis III CUNY Hunter College

• This includes the introduction of hashes, heaps, various forms of trees, and graphs. It also revisits recursion and the sorting problem from a higher perspective than was presented in the prequels. On top of this, it is intended to introduce methods of algorithmic analysis.

 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!

 Advanced Algorithms MIT

• Advanced course in algorithms by Dr. David Karger covering topics such as amortization, randomization, fingerprinting, word-level parallelism, bit scaling, dynamic programming, network flow, linear programming, fixed-parameter algorithms, and approximation algorithms.
• Register on  to access the .

 Advanced Data Structures MIT

• This is an advanced DS course, you must be done with the course before attempting this one.
•  Contains videos from sp2012 version, but there isn't much difference.
•  contains the calendar as well.

 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.

 Algorithm Design and Analysis UC Davis

• Taught by  in 2010, this course is an undergraduate introduction to algorithm design and analysis. It features traditional topics, such as Big Oh notation, as well as an importance on implementing specific algorithms. Also featured are sorting (in linear time), graph algorithms, depth-first search, string matching, dynamic programming, NP-completeness, approximation, and randomization.

 Graduate Level Algorithm Design and Analysis UC Davis

• This is the graduate level complement to the ECS 122A undergraduate algorithms course by  in 2011. It assumes an undergrad course has already been taken in algorithms, and, while going over some undergraduate algorithms topics, focuses more on increasingly complex and advanced algorithms.

Programming Languages / Compilers

 Programming Languages University of Washington

• Covers non-imperative paradigms and languages such as Ruby, Racket, and ML and the fundamentals of programming languages.

 Functional Programming Princeton University

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

 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.
•  semester also available, with more exercises

 Functional Systems in Haskell Stanford University

• Building software systems in Haskell
• 3 Assignments: , , 

 Hack your language! UC Berkeley

• Introduction to programming languages by designing and implementing domain-specific languages.

 Data Structures and Functional Programming Cornell University

• Another course that uses OCaml to teach alternative programming paradigms, especially functional and concurrent programming.

 Programming Languages Brown University

• Course by Prof. Krishnamurthi (author of ) and numerous other  on programming languages. Uses a custom designed  programming language to teach the concepts. There was an  hosted in 2012, which includes all lecture videos for you to enjoy.

 Programming Languages and Compilers Univ of Illinois, Urbana-Champaign

• Course that uses OCaml to teach functional programming and programming language design.

 Compilers University of Utah

• If you're a fan of Prof Matt's writing on his  you ought to give this a shot. The course covers the design and implementation of compilers, and it explores related topics such as interpreters, virtual machines and runtime systems. Aside from the Prof's witty take on  the page has tons of interesting links on programming languages, parsing and compilers.

 Programming Languages and Compilers University of Virginia

• Course that uses OCaml to teach functional programming and programming language design. Each assignment is a part of an interpreter and compiler for an object-oriented language similar to Java, and you are required to use a different language for each assignment (i.e., choose 4 from Python, JS, OCaml, Haskell, Ruby).

 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.

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'14 version by professor .
•  7 HWs with answer set as well

 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.

 Systems Programming Univ of Illinois, Urbana-Champaign

• Learn how to write programs that take full advantage of operating system support in the C programming language

 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.

 Distributed Systems Carnegie-Mellon University

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

 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!
•  - Just dogit clone git://g.csail.mit.edu/6.824-golabs-2014 6.824

 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.

 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

 Engineering Distributed Systems Carnegie-Mellon University

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

 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.

 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  is also a gem and recommended as a must read in Google's tutorial on 

 Computer Architecture 3 CUNY Hunter College

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

 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.

 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.

 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.