University Courses

Introduction to software engineering using the

Machine Learning** *Carnegie Mellon University*

Probabilistic Graphical Models** *Carnegie Mellon University*

Deep Learning** *Carnegie Mellon University*

Fundamentals of Computer Networks** *CMU*

Introduction to Computer Systems (ICS)** *Carnegie-Mellon University*

Cloud Computing (ICS)** *Carnegie-Mellon University*

Operating System Design and Implementation** *Carnegie-Mellon University*

Parallel Computer Architecture and Programming** *Carnegie-Mellon University*

Distributed Systems** *Carnegie-Mellon University*

Database Systems** *Carnegie-Mellon University*

Algorithms** *Carnegie Mellon University*

Database Systems** *Carnegie-Mellon University*

Engineering Distributed Systems** *Carnegie-Mellon University*

Algorithms** *University of Virginia*

Introduction to Computer Architecture** *CMU*

Browser Security** *Stanford*

(slightly better)*

Structure and Interpretation of Computer Programs** *MIT*

Computation Structures** *MIT*

Software Construction, Fall 2016** *MIT*

Introduction to Algorithms** *MIT*

This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application. Topics include divide-and-conquer, randomization, dynamic programming, greedy algorithms, incremental improvement, complexity, and cryptography. This course assumes that students know how to analyze simple algorithms and data structures from having taken . It introduces students to the design of computer algorithms, as well as analysis ...

Great Ideas in Theoretical Computer Science** *MIT*

Design and Analysis of Algorithms** *MIT*

Distributed Systems** *MIT*

Operating Systems** *MIT*

Advanced Data Structures** *MIT*

Advanced Algorithms** *MIT*

Advanced Algorithms** *MIT*

Computer and Network Security** *MIT*

Computer Systems Security** *MIT*

The Society of Mind** *MIT*

Adventures in Advanced Symbolic Programming** *MIT*

Hacking a Google Interview** *MIT*

This is an advanced DS course, you must be done with the course before attempting this one.

Monte Carlo Methods and Stochastic Optimization** *Harvard University*

The lectures cover all the material in which is a more approachable version of the (or ESL) book.

Just do `git clone git://g.csail.mit.edu/6.824-golabs-2014 6.824`

: Extensive programming assignments, using MIT/GNU Scheme. Students should have significant programming experience in Scheme, Common Lisp, Haskell, CAML or other "functional" language.

contains the calendar as well.

available on Github.

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.

Taught by , , and

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

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.

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.

Computer Vision** *University of Central Florida*

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.

Introduction to Haskell** *Penn Engineering*

Rust Programming** *UPenn*

Offensive Computer Security** *Florida State University*

Software Foundations** *University of Pennsylvania*

Computer Vision and Computational Photography** *University of Pennsylvania*

Introduction to Compilers** *Univ of Maryland*

Computer Graphics** *Carnegie Mellon University*

Machine Learning** *Columbia University*

Principles of Programming Languages** *Politecnico di Milano* - Lecture Notes - Readings

Functional Programming** *Princeton University*

The Beauty and Joy of Computing** *UC Berkeley*

Open Source Software Construction** *UC Riverside*

Computer Science 101** *Stanford University*

Mathematical Foundations of Computing** *Stanford University*

Programming Methodology** *Stanford University*

Programming Abstractions** *Stanford University*

Computer Organization & Systems** *Stanford University*

Programming Paradigms** *Stanford University*

Object Oriented System Design** *Stanford*

Programming Practice Using Scala** *KAIST*

Data Science** *Harvard University*

Fundamental Programming Concepts** *Cornell University*

Introduction to Computing Using Python** *Cornell University*

Introduction to Computing Using Matlab** *Cornell University*

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

Transition to OO Programming** *Cornell University*

Transition to Python** *Cornell University*

Operating Systems** *Stanford University*

and CS2420-20 **Computer Science I and II for Hackers** *University of Utah*

Compiler construction** *Stanford University*

Computer and Network Security** *Stanford*

Learning from Data** *Caltech*

Computer Security** *UC Berkeley*

Operating Systems and Systems Programming** *UC Berkeley*

Hack your language!** *UC Berkeley*

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

Computer Networks** *UC Berkeley*

Programming Languages** *Brown University*

Discrete Structures** *Univ of Illinois Urbana-Champaign*

GPU Programming** *Caltech*

Introduction to Database Systems** *UC Berkeley*

Introduction to Artificial Intelligence** *UC Berkeley*

Introduction To Machine Learning** *UC Berkeley*

Android App Development, Spring 2016** *Stanford University*

Developing Applications for iOS** *Stanford University*

Unix Tools & Scripting** *Cornell University*

Object-Oriented Programming and Data Structures** *Cornell University*

Program & Data Representation** *University of Virginia*

Purely Functional Data Structures In Elm** *University of Chicago*

Introduction to Robotics** *Stanford University*

Advanced Algorithms** *Harvard University*

Deep Learning for Natural Language Processing** *Stanford University*

Algorithms for Big Data** *Harvard University*

Convolutional Neural Networks for Visual Recognition** *Stanford University*

Functional Systems in Haskell** *Stanford University*

Systems Programming (Spring 2016)** *Univ of Illinois, Urbana-Champaign*

Security Modeling and Analysis** *Stanford*

A Second Course in Algorithms** *Stanford University*

Internet/Network Security** *UC Berkeley*

Advanced Topics in Computer Systems** *UC Berkeley*

Foundations of Cryptography** *UC Berkeley*

Complexity Theory** *UC Berkeley*

Advanced Robotics** *UC Berkeley*

Cutting-edge Web Technologies** *Berkeley*

Data Structures and Functional Programming** *Cornell University*

Data Structures and Functional Programming** *Cornell University*

Introduction to Computer Game Development** *Cornell University*

Introduction to Scientific Computing** *Cornell University*

Computer System Organization and Programming** *Cornell University*

Algorithms & Models of Computation (Fall 2014)** *University of Illinois Urbana-Champaign*

3D Reconstruction with Computer Vision** *UTexas*

Statistical and Discrete Methods for Scientific Computing** *University of Texas*

Software Architecture Design** *Bilkent University*

Introduction to Compilers** *Cornell University*

Advanced Topics in Computer Game Development** *Cornell University*

Analytics-driven Game Design** *Cornell University*

Programming Languages and Compilers** *Univ of Illinois, Urbana-Champaign*

Distributed Systems** *Univ of Illinois, Urbana-Champaign*

Information Retrieval** *Cornell University*

Web Information Systems** *Cornell University*

Programming Languages** *Northeastern University*

Operating Systems** *Cornell University*

Operating Systems** *University of Virginia*

Real-Time Programming** *University of Waterloo*

Programming Languages and Compilers** *University of Virginia*

Introduction to Computer Graphics** *Cornell University*

Introduction to Computer Vision** *Cornell University*

Foundations of Artificial Intelligence** *Cornell University*

Fundamental Algorithms** *Univ of Illinois, Urbana-Champaign*

Machine Learning** *Cornell University*

Machine Learning for Data Science** *Cornell University*

Introduction to Theory of Computing** *Cornell University*

Quantum Information Processing** *Cornell University*

Introduction to Analysis of Algorithms** *Cornell University*

Applied Logic** *Cornell University*

Introduction to Computer Science** *Harvard University*

Intro to Game Developement** *Harvard University*

Network Programming Languages** *Cornell University*

Scripting Languages** *Cornell University*

Software Engineering** *Cornell University*

Applications of Parallel Computers** *Cornell University*

Cloud Computing** *Cornell University*

System Security** *Cornell University*

Compilers** *University of Utah*

Computational Techniques for Analyzing Clinical Data** *Cornell University*

Evolutionary Computation** *Cornell University*

Types and Semantics** *Cornell University*

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

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

Data Structures** *UC Berkeley*

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

Datacenter Networks and Services** *Cornell University*

Realistic Image Synthesis** *Cornell University*

Computational Photography** *Cornell University*

Computational Motion** *Cornell University*

Computer Vision** *Cornell University*

Advanced Artificial Intelligence** *Cornell University*

Theory of Computing** *Cornell University*

Algorithmic Game Theory** *Cornell University*

Functional Design and Programming** *San Diego State University*

Principles of Compiler Design** *Swathmore College*

Introduction to Game Development** *Tufts University*

Introduction to Programming Languages** *Swathmore College*

Introduction to Competitive Programming** *Stanford University*

CS for All** *Harvey Mudd College*

Tensorflow for Deep Learning Research** *Stanford University*

Mining Massive Data Sets** *Stanford University*

Information Retrieval and Web Search** *Stanford University*

This course picks up where Harvard College’s 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 , as well as languages like Lua and C#. By class’s end, s...

CPython internals: A ten-hour codewalk through the Python interpreter source code** *University of Rochester*

Programming Foundations I** *University of Arkansas (Fayetteville)*

Programming Paradigms** *University of Arkansas (Fayetteville)*

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.

Data Structures and Object Oriented Design** *University of Southern California (USC)*

Introduction to Computer Graphics** *Brown University*

Software Design and Analysis I**

Software Design and Analysis II** *CUNY Hunter College*

Software Design and Analysis III**

Computer Architecture 3** *CUNY Hunter College*

UNIX System Programming (formerly UNIX Tools)** *CUNY Hunter College*

Parallel Computing** *CUNY Hunter College*

Modern Binary Exploitation** *Rensselaer Polytechnic Institute*

Malware Analysis** *Rensselaer Polytechnic Institute*

Understanding Computers and the Internet** *Harvard University Extension College*

Graduate Computer Graphics** *New York University*

Operating Systems** *NYU*

Web Programming** *University of Washington*

Software Design and Implementation** *University of Washington*

Programming Languages** *University of Washington*

Analysis of Algorithms** *Stony Brook University*

Compiler Construction** *University of Washington*

Distributed Systems** *University of Washington*

Convex Optimization** *Stanford University*

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.

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.

Operating Systems course by the Chair of EECS, UC Berkeley

Deep Learning for Natural Language Processing** *University of Oxford*

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

Discrete Mathematics and Functional Programming** *Wheaton College*

Taught by

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

Deep Learning** *New York University*

Programming for Performance** *University of Waterloo*

Embedded Systems using the Renesas RX63N Processor** *University of North Carolina at Charlotte*

Algorithm Design and Analysis** *UC Davis*

Graduate Level Algorithm Design and Analysis** *UC Davis*

The course can also be taken from .

Introduction to Matrix Methods** *Stanford University*

Computer & Network Security** *University of Michigan*

Big Data Analytics & Advanced Big Data Analytics** *Columbia University*

Deep Learning for Computer Vision and Natural Language Processing** *Columbia University*

The lectures cover all the material in which is a more approachable version of the (or ESL) book.

GIS & Spatial Analysis** *UC Santa Barbara*

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.

Fast.ai / University of San Francisco*

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

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.

(includes lecture materials and labs)

Introduction to Operating Systems** *SUNY University at Buffalo, NY*

The course's OpenCourseware resides

Videos: Videos list can be found

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 to download them.

7 HWs with answer set as well

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.