There is a lot of hidden treasure lying within university pages scattered across the internet. This list is an attempt to bring to light those awesome courses which make their high-quality material i.e. assignments, lectures, notes, readings & examinations available online for free.
- Systems
- Programming Languages / Compilers
- Algorithms
- CS Theory
- Introduction to CS
- Machine Learning
- Misc
- Lecture Videos
- Lecture Notes
- Assignments / Labs
- Readings
- CS 107 Computer Organization & Systems Stanford University
- CIS 194 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
- Assignments & Lectures
- Clojure 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
- COS 326 Functional Programming Princeton University
- Covers functional programming concepts like closures, tail-call recursion & parallelism using the OCaml programming language
- Lectures
- Assignments
- CS 164 Hack your language! UC Berkeley
- Introduction to programming languages by designing and implementing domain-specific languages.
- Lecture Videos
- Code for Assignments
- CS 173 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
- CS 240h Functional Systems in Haskell Stanford University
- Building software systems in Haskell
- Lecture Slides
- 3 Assignments: Lab1, Lab2, Lab3
- CS 3110 Data Structures and Functional Programming Cornell University
- Another course that uses OCaml to teach alternative programming paradigms, especially functional and concurrent programming.
- Lecture Slides
- Assignments
- CS 421 Programming Languages and Compilers Univ of Illinois, Urbana-Champaign
- Course that uses OCaml to teach functional programming and programming language design.
- Lectures
- Videos
- Assignments
- Exams
- CS 4610 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).
- Lecture Notes
- Assignments
- CS 5470 Compilers University of Utah
- If you're a fan of Prof Matt's writing on his fantastic blog 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 cheating the page has tons of interesting links on programming languages, parsing and compilers.
- Lecture Notes
- Projects
- CSE 341 Programming Languages University of Washington
- Covers non-imperative paradigms and languages such as Ruby, Racket, and ML and the fundamentals of programming languages.
- Lectures
- Assignments and Tests
- CS 61B 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.
- Labs
- Lecture Videos on Youtube
- CS 473/573 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
- CS 2150 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
- CSCI 104 Data Structures and Object Oriented Design
- CSCI 135 Software Design and Analysis I
- 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.
- Lectures and Assignments
- CSCI 235 Software Design and Analysis II
- 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.
- Lectures and Assignments
- CSCI 335 Software Design and Analysis III
- 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.
- Lectures and Assignments
- CSE 373 Analysis of Algorithms Stony Brook University
- Prof Steven Skiena's no stranger to any student when it comes to algorithms. His seminal book has been touted by many to be best for getting that job in Google. In addition, he's also well-known for tutoring students in competitive programming competitions. If you're looking to brush up your knowledge on Algorithms, you can't go wrong with this course.
- Lecture Videos
- CS 97SI 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 Assignments
- ECS 122A Algorithm Design and Analysis UC Davis
- Taught by Dan Gusfield 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.
- Syllabus
- Lecture Videos
- Assignments
- ECS 222A Graduate Level Algorithm Design and Analysis UC Davis
- This is the graduate level complement to the ECS 122A undergraduate algorithms course by Dan Gusfield 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.
- Lecture Videos
- Syllabus
- Assignments
- 6.INT 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.
- Handouts
- Topics Covered
- 6.851 Advanced Data Structures MIT
- This is an advanced DS course, you must be done with the Advanced Algorithms course before attempting this one.
- Lectures Contains videos from sp2012 version, but there isn't much difference.
- Assignments contains the calendar as well.
- 6.854/18.415J 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 NB to access the problem set and lectures.
- 15-451/651 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.
- CIS 500 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
- CS 103 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
- CS 173 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
- CS 276 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.
- CS 278 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.
- CS 374 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
- CSCE 3193 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
- CS 10 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
- CS 50 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.
- Lectures
- CS 101 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.
- CS 106A 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
- CS 106B 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
- CS 107 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
- CSCE 2004 Programming Foundations I University of Arkansas (Fayetteville)
- Introductory course for students majoring in computer science or computer engineering. Software development process: problem specification, program design, implementation, testing and documentation. Programming topics: data representation, conditional and iterative statements, functions, arrays, strings, file I/O, and classes. Using C++ in a UNIX environment.
- Syllabus
- Notes
- Assignments
- Practice Exams
- CSCE 2014 Programming Foundations 2 University of Arkansas (Fayetteville)
- This course continues developing problem solving techniques by focusing on fundamental data structures and associated algorithms. Topics include: abstract data types, introduction to object-oriented programming, linked lists, stacks, queues, hash tables, binary trees, graphs, recursion, and searching and sorting algorithms. Using C++ in a UNIX environment.
- Syllabus
- Assignments
- Practice Exams
- 6.001 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.
- Lectures
- Textbook (epub, pdf)
- IDE
- CS1410-2 and CS2420-20 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 explainations 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
- 11-785 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.
- 15-781 Machine Learning Carnegie Mellon University
- Taught by one of the leading experts on Machine Learning - Tom Mitchell
- Lectures
- Project Ideas and Datasets
- CS 109 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
- 2013 Lectures (slightly better)
- COMS 4771 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
- CVX 101 Convex Optimization Stanford University
- CIS 581 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.
- Lectures
- Assignments
- CIS 4930 / CIS 5930 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
- CS 75 Introduction to Game Development Tufts University
- The course taught by Ming Y. Chow teaches game development initially in PyGame through Python, before moving on to addressing all facets of game development. Topics addressed include game physics, 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
- Text Lectures
- Assignments
- Labs
- CS 100 Open Source Software Construction UC Riverside
- This is a course on how to be a hacker. Your first four homework assignments walk you through the process of building your own unix shell. You'll be developing it as an open source project, and you will collaborate with each other at various points.
- Github Page
- Assignments
- CS 193p Developing Applications for iOS Stanford University
- Updated for iOS 7. Tools and APIs required to build applications for the iPhone and iPad platform using the iOS SDK. User interface designs for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Objective-C programming language. Other topics include: object-oriented database API, animation, multi-threading and performance considerations.
- Prerequisites: C language and object-oriented programming experience
- Recommended: Programming Abstractions
- Updated courses for iOS8 - Swift
- CS 223A 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.
- Lectures
- Assignments
- CS 378 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
- CS 411 Software Architecture Design Bilkent University
- This course teaches the basic concepts, methods and techniques for designing software architectures. The topics include: rationale for software architecture design, modeling software architecture design, architectural styles/patterns, architectural requirements analysis, comparison and evaluation of architecture design methods, synthesis-based software architecture design, software product-line architectures, domain modeling, domain engineering and application engineering, software architecture implementation, evaluating software architecture designs.
- CS 5150 Software Engineering Cornell University
- Introduction to the practical problems of specifying, designing, building, testing, and delivering reliable software systems
- Lectures
- CSE 154 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.
- Lectures
- Assignments
- EECS 588 Computer & Network Security University of Michigan
- Taught by J. Alex Halderman who has analyzed the security of Electronic Voting Machines in the US and over seas.
- This intensive research seminar covers foundational work and current topics in computer systems security.
- Readings
- ESM 296-4F GIS & Spatial Analysis UC Santa Barbara
- Taught by James Frew, Ben Best, and Lisa Wedding
- 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
- GitHub (includes lecture materials and labs)
- ICS 314 Software Engineering University of Hawaii
- Taught by Philip Johnson
- Introduction to software engineering using the "Athletic Software Engineering" pedagogy
- Readings
- Experiences
- Assessments
- IGME 582 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.
- I485 / H400 Biologically Inspired Computation Indiana University
- Course taught by Luis Rocha about the multi-disciplinary field algorithms inspired by naturally occurring phenomenon. This course provides introduces the following areas: L-systems, Cellular Automata, Emergence, Genetic Algorithms, Swarm Intelligence and Artificial Immune Systems. It's aim is to cover the fundamentals and enable readers to build up a proficiency in applying various algorithms to real-world problems.
- Lectures
- Assignments
- Open Sourced Elective: Database and Rails Intro to Ruby on Rails University of Texas
- An introductory course in Ruby on Rails open sourced by University of Texas' CS Adjunct Professor, Richard Schneeman.
- Lectures
- Assignments
- Videos
- Info 290 Analyzing Big Data with Twitter UC Berkeley school of information
- In this course, UC Berkeley professors and Twitter engineers provide lectures on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter's data. Topics include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing.
- Lecture Videos
- Previous Years coursepage
- EECS E6893 & EECS E6895 Big Data Analytics & Advanced Big Data Analytics Columbia University
- Students will gain knowledge on analyzing Big Data. It serves as an introductory course for graduate students who are expecting to face Big Data storage, processing, analysis, visualization, and application issues on both workplaces and research environments.
- Taught by Dr. Ching-Yung Lin
- Course Site
- Assignments - Assignments are present in the Course Slides