Open Source Computer Science Courses from Top Universities in the US.

A collection of open-source, free courses from top universities in the United States offered on YouTube to serve as a comprehensive computer science degree. Become a self-taught computer scientist!

Contents

• Summary
• Curriculum

Summary

The purpose of this is to serve as a meticulously curated list of open-source and accessible high quality courses in computer science, with lectures available on YouTube from world-renowned institutions such as MIT, Harvard, Stanford, and UC Berkeley. This repository is not aimed to provide the resources of a full course (although many of these courses are offered for free through Coursera and EdX on Open Source Society University); it is a thorough, comprehensive exploration of everything from computer science fundamentals and principles to advanced topics designed for those who aspire to gain a deep and holistic understanding of the field for free, at their own pace on YouTube. Also, while this is intended to be from the university structured learning perspective, that style may not be for everyone. I recommend checking out CrashCourse for high-level overviews, taking a look at freeCodeCamp as they have some great videos, as well as reading and learning from engineering blogs at top companies for alternatives.

Curriculum Structure:

• Introductory Computer Science: This section is for learners new to the field, offering a taste of what computer science entails.
• Core Computer Science: This comprehensive segment covers the essentials, paralleling the first three years of a typical computer science undergraduate program.
• Advanced Computer Science: Reflecting the senior year of a computer science degree, this section offers advanced courses for learners to specialize in areas of interest.

Duration and Pace:

While self-paced, the program is designed to be completed in approximately two years with a commitment of around 20 hours per week. Learners can track their progress using this spreadsheet, adapting their study schedule to personal commitments.

Cost:

The core material, sourced from YouTube lectures, is entirely free. Some courses may recommend textbooks or supplementary materials which may incur costs, but these are optional. The community is encouraged to also open PRs with open-source or freely available textbooks, but EbookFoundation also has a great selection of resources here if that is your preferred learning style.

Contributing to this Project:

The repository welcomes contributions from its community. Whether it’s suggesting additional resources, helping to refine existing content, or offering peer support, every contribution enriches the learning experience for all.

Curriculum

While the curriculum is designed to be done in order, you can model your learning based on your current educational background. Although I would still recommend starting from the beginning; the refreshers from these courses can't hurt.

Computer Science Fundamentals

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
Intro to Computer Science CS50	Harvard	12 Lectures	4-6 hours/week	self-paced	none
Intro to Computer Science 6.001	MIT	38 Micro-Lectures	2-3 hours/week	self-paced	none
Intro to Computer Science CS105	Stanford	69 Micro-Lectures	3-4 hours/week	self-paced	none
Programming Methodology CS106A	Stanford	28 Lectures	4-6 hours/week	self-paced	none
Computation Structures	MIT	172 Micro-Lectures or 26 full-length Lectures	4-5 hours/week	self-paced	none
Introduction to Computer Networking CS144	Stanford	24 Lectures	1-2 hours/week	self-paced	none
Computer Organization & Systems CS107	Stanford	15 Lectures	2-3 hours/week	self-paced	none
Operating Systems and Systems Programming CS162	UC Berkeley	27 Lectures	4-6 hours/week	self-paced	none
Introduction to Databases	Stanford	12 Micro-Lectures	0-1 hours/week	self-paced	none

Computer Science Math Core Topics

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
Introduction to Probability for Computer Scientists CS109	Stanford	29 Lectures	4-5 hours/week	self-paced	none
Probability and Statistics 110	Harvard	35 Lectures	4-5 hours/week	self-paced	none
Mathematics for Computer Science 6.042J	MIT	111 Micro-Lectures	3-4 hours/week	self-paced	none
Discrete Mathematics Math 55	UC Berkeley	28 Lectures	4-5 hours/week	self-paced	none
Discrete Mathematics and Probability Theory CS 70	UC Berkeley	28 Lectures	4-5 hours/week	self-paced	none
A Vision of Linear Algebra	MIT	8 Lectures	1-2 hours/week	self-paced	none
Introduction to Applied Linear Algebra	Stanford	54 Micro-Lectures	3-4 hours/week	self-paced	none
Linear Algebra	Princeton	15 Lectures	2-3 hours/week	self-paced	none
Matrix Calculus for Machine Learning and Beyond 18.S096	MIT	17 Lectures	1-3 hours/week	self-paced	none
Single Variable Calculus 18.01	MIT	35 Lectures	4-6 hours/week	self-paced	none
Multivariable Calculus 18.02	MIT	35 Lectures	4-6 hours/week	self-paced	Single Variable Calculus
Multivariable Calculus Math 53	UC Berkeley	25 Lectures	4-6 hours/week	self-paced	Single Variable Calculus
Multivariable Calculus	Princeton	14 Lectures	4-6 hours/week	self-paced	Single Variable Calculus

Computer Science Core Topics

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
Introduction to Algorithms 6.006	MIT	32 Lectures	4-6 hours/week	self-paced	CS Fundamentals Course + Discrete Mathematics strongly recommended.
Computer Architecture 18-447	Carnegie-Mellon	39 Lectures	4-6 hours/week	self-paced	CS fundamentals strongly recommended.
Human-Computer Interaction CS547	Stanford	259 Lectures	5-6 hours/week+	self-paced	CS fundamentals strongly recommended.
Introduction to Database Systems 15-445/645	Carnegie-Mellon	25 Lectures	4-5 hours/week	self-paced	CS fundamentals strongly recommended.
Design and Analysis of Algorithms 6.046J	MIT	34 Lectures	4-6 hours/week	self-paced	CS fundamentals, intro to algorithms strongly recommended.
Operating System Engineering 6.828	MIT	12 Lectures found	2-3 hours/week	self-paced	CS fundamentals strongly recommended.
Database Systems 6.830/6.814	MIT	14 Lectures found	3-4 hours/week	self-paced	CS fundamentals strongly recommended.

Computer Science Advanced Topics

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
Advanced Algorithms COMPSCI 224	Harvard	25 Lectures	4-6 hours/week	self-paced	CS Intro to Algorithms Course + Discrete Mathematics minimum.
Algorithms for Big Data COMPSCI 229r	Harvard	25 Lectures	4-5 hours/week	self-paced	CS Intro to Algorithms Course + Discrete Mathematics minimum.
Advanced Database Systems 15-721	Carnegie-Mellon	23 Lectures	3-4 hours/week	self-paced	CS fundamentals + core required.
Distributed Systems 6.824	MIT	20 Lectures	2-4 hours/week	self-paced	CS Intro to systems + fundamentals courses required.
Distributed Computer Systems CS 436	UWaterloo	24 Lectures	4-6 hours/week	self-paced	CS fundamentals + systems/architecture courses required.
Performance Engineering of Software Systems 6.172	MIT	23 Lectures	4-5 hours/week	self-paced	CS Intro to systems + fundamentals courses required.
Theory of Computation 18.404J	MIT	25 Lectures	4-5 hours/week	self-paced	CS fundamentals + math core + CS core required.

Specialities

This section is for more specialized tracks in security, robotics, machine learning/artificial intelligence, and more.

Machine Learning/Artifical Intelligence (ML/AI)

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
Introduction to Artificial Intelligence CS 188	UC Berkeley	25 Lectures	3-4 hours/week	self-paced	CS fundamentals
Artificial Intelligence CS221	Stanford	19 Lectures	3-4 hours/week	self-paced	CS fundamentals
Machine Learning CS229	Stanford	19 Lectures	2-4 hours/week	self-paced	CS fundamentals
Machine Learning Theory CS229M	Stanford	20 Lectures	3-5 hours/week	self-paced	CS fundamentals
Intro to Deep Learning 6.S191	MIT	63 Lectures	6-7 hours/week	self-paced	CS fundamentals
Deep Learning CS230	Stanford	10 Lectures	2-3 hours/week	self-paced	CS fundamentals
Natural Language Processing with Deep Learning CS224N	Stanford	23 Lectures	3-4 hours/week	self-paced	CS fundamentals
Natural Language Understanding XCS224U	Stanford	50 Mixed-Lectures	4-5 hours/week	self-paced	CS fundamentals + intro to ML
Machine Learning with Graphs CS224W	Stanford	60 Micro-Lectures	3-4 hours/week	self-paced	CS fundamentals + Discrete Mathematics + Advanced Algorithms
Deep Learning for Self-Driving Cars 6.094	MIT	9 Lectures	2-3 hours/week	self-paced	CS fundamentals + intro to DL or ML
Modern Computer Vision CS198-126	UC Berkeley	22 Lectures	4-5 hours/week	self-paced	CS fundamentals + intro to DL or ML
Reinforcement Learning CS234	Stanford	15 Lectures	2-3 hours/week	self-paced	CS fundamentals + intro to ML
Deep Multi-task and Meta Learning CS330	Stanford	17 Lectures	4 hours/week	self-paced	CS fundamentals + intro to DL

Robotics

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
Intro to Robotics	Princeton	24 Lectures	4-6 hours/week	self-paced	none
Deep Learning for Self-Driving Cars 6.094	MIT	9 Lectures	2-3 hours/week	self-paced	CS fundamentals + intro to DL or ML
Reinforcement Learning CS234	Stanford	15 Lectures	2-3 hours/week	self-paced	CS fundamentals + intro to ML

Aerospace

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
Fundamentals of Systems Engineering	MIT	12 Lectures	2-3 hours/week	self-paced	none

Cybersecurity

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
Computer Systems Security 6.858	MIT	26 Lectures	3-4 hours/week	self-paced	CS fundamentals + cs systems fundamentals recommended.

Data Analytics & Data Science

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
Statistical Learning with R	Stanford	104 Micro-Lectures	2-4 hours/week	self-paced	CS fundamentals

Bonus Topics and Courses

Courses	School	Duration	Time Commitment	Frequency	Prerequisites
How to Start a Startup	YCombinator/Stanford	21 Lectures	3-5 hours/week	self-paced	none
Fundamentals of Physics PHYS 200	Yale	24 Lectures	4-5 hours/week	self-paced	Calculus
Physics I: Classical Mechanics 8.01x	MIT	40 Lectures	4-6 hours/week	self-paced	Calculus
Physics II: Electricity & Magnetism 8.02x	MIT	40 Lectures	4-6 hours/week	self-paced	Calculus + Physics I
Physics III: Vibrations and Waves 8.03x	MIT	24 Lectures	3-4 hours/week	self-paced	Calculus + Physics I & II
Quantum Physics I 8.04	MIT	115 Micro-Lectures	4-6 hours/week	self-paced	Calculus + Physics
General Relativity 8.962	MIT	23 Lectures	3-5 hours/week	self-paced	Math + Physics
Atomic and Optical Physics I 8.421	MIT	25 Lectures	4-5 hours/week	self-paced	Math + Physics