Computer Science Roadmap (AI Track)

• Learning How to Learn

Intro to CS

• CS50 - Introduction to Computer Science - Harvard
• 6.0001 - Introduction to Computer Science and Programming in Python, Fall 2016 - MIT
• 6.0002 - Introduction to Computational Thinking and Data Science, Fall 2016 - MIT

Programming

• CS61A - Structure and Interpretation of Computer Programs (Python + Scheme) - UC Berkeley
• CS61A - Structure and Interpretation of Computer Programs (Scheme), 2010 - UC Berkeley
• CS106A - Programming Methodology (Java) - Stanford
• CS106B - Programming Abstractions (C++) - Stanford
• CS107 - Programming Paradigms - Stanford
• CSE341 - Programming Languages, Spring 2013 - University of Washington
• CS212 - Design of Computer Programs - Peter Norvig
• CS210 - Functional Programming in Scala - EPFL
• 6.S095 - Programming for the Puzzled, Spring 2018 - MIT

Maths

• Calculus

  • 18.01 - Single Variable Calculus, Fall 2006 - MIT
  • 18.02 - Multivariable Calculus, Fall 2007 - MIT
  • 18.03 - Differential Equations, Spring 2010 - MIT

• Linear Algebra

  • 18.06 - Linear Algebra, Fall 2011 - MIT
  • Linear Algebra Review - CMU
  • Computational Linear Algebra

• Probability and Statistics

  • 6.041 - Probabilistic Systems Analysis and Applied Probability, Fall 2013 - MIT
  • STAT110 - Probability - Harvard
  • 18.650 - Statistics for Applications, Fall 2016 - MIT
  • 36-705 - Intermediate Statistics, Fall 2016 - CMU
  • 6.262 - Discrete Stochastic Processes, Spring 2011 - MIT
  • AM207 - Stochastic Methods for Data Analysis, Inference and Optimization, 2016 - Harvard

• Discrete Maths

  • 6.042J - Mathematics for Computer Science, Fall 2010 - MIT

• Opmitisation

  • EE364A - Convex Optimization I - Stanford
  • EE364B - Convex Optimization II - Stanford
  • 10-725 - Convex Optimization, Fall 2016 - CMU
  • 10-801 - Advanced Optimization and Randomized Methods, Spring 2014 - CMU

• Maths for ML (mostly books)

  • 10-606 - Math Background for Machine Learning, Fall 2017 - CMU
  • 18-657 - Mathematics of Machine Learning, Fall 2015 - MIT
  • CO-496 - Mathematics for Inference and Machine Learning - Imperial College
  • Book - Mathematics for Machine Learning - Imperial College
  • Book - Mathematics for Machine Learning - UC Berkeley

• Other

  • MOOC - Introduction to Logic - Stanford
  • 18.S096 - Topics in Mathematics with Application in Finance, Fall 2013 - MIT
  • MOOC - Game Theory - Stanford
  • MOOC - Discrete Optimization - University of Melbourne
  • Operations Research - SUNY Binghamton University
  • Linear Programming, Fall 2016 - Penn State University

Data Structures and Algorithms

• CS61B - Data Structures, Spring 2019 - UC Berkeley
• 6.006 - Introduction to Algorithms, Fall 2011 - MIT
• COS226 - Algorithms - Princeton
• 6.046J - Design and Analysis of Algorithms, Spring 2015 - MIT
• CS161 - Algorithms: Design and Analysis, Part 1 - Stanford
• CS161 - Algorithms: Design and Analysis, Part 2 - Stanford
• 6.851 - Advanced Data Structures, Spring 2012 - MIT
• CS224 - Advanced Algorithms, Fall 2014 - Harvard
• CS229R - Algorithms for Big Data, Fall 2015 - Harvard
• CS170 - Efficient Algorithms and Intractable Problems, Fall 2020 - UC Berkeley

Computer Architecture

• CS61C - Great Ideas in Computer Architecture, Spring 2015 - UC Berkeley
• CS152 - Computer Architecture and Engineering, Spring 2016 - UC Berkeley
• 18-447 - Computer Architecture, Spring 2015 - CMU
• 15-418 - Parallel Computer Architecture and Programming, Spring 2016 - CMU
• CS267 - Applications of Parallel Computers, Spring 2016 - UC Berkeley

System Programming

• 15-213 - Introduction to Computer Systems, Fall 2015 - CMU
• CS162 - Operating Systems and System Programming, Spring 2015 - UC Berkeley
• 6.824 - Distributed Systems, Spring 2020 - MIT
• CS436 - Distributed Computer Systems, Winter 2012 - University of Waterloo

Software Engineering

• CS169 - Software Engineering, Spring 2015 - UC Berkeley
• CS6310 - Software Architecture & Design - Georgia Tech
• CS5150 - Software Engineering, Fall 2014 - Cornell
• CS164 - Software Engineering, Spring 2014 - Harvard

Database Systems

• CS145 - Introduction to Databases - Stanford
• CS186 - Introduction to Database Systems, Spring 2015 - UC Berkeley
• 15-445 - Introduction to Database Systems, Fall 2017 - CMU
• 15-721 - Advanced Database Systems, Spring 2018 - CMU

Computer Networks

• 14-740 - Fundamentals of Computer Networks, Fall 2017 - CMU
• CS144 - Introduction to Computer Networking - Stanford

Compilers

• CS143 - Compilers, Fall 2014 - Stanford
• CS164 - Programming Languages and Compilers, Spring 2012 - UC Berkeley

Theoretical CS

• 15-251 - Great Ideas in Theoretical Computer Science - CMU
• CS154 - Automata Theory - Stanford
• Category Theory, Summer 2016

Machine Learning and Artificial Intelligence

• Artificial Intelligence

  • CS188 - Introduction to Artificial Intelligence, Fall 2018 - UC Berkeley
  • 6.034 - Artificial Intelligence, Fall 2010 - MIT
  • 15-780 - Graduate Artificial Intelligence, Spring 2017 - CMU
  • CS221 - Artificial Intelligence: Principles and Techniques, Fall 2019

• Machine Learning

  • STATS216 - Statistical Learning, Winter 2016 - Stanford
  • CS229 - Machine Learning - Stanford
  • CS155 - Machine Learning & Data Mining, Winter 2017 - Caltech
  • CS156 - Learning from Data, Caltech
  • 10-601 - Introduction to Machine Learning (MS), Spring 2015 - CMU
  • 10-701 - Introduction to Machine Learning (PhD), Spring 2011 - CMU
  • 10-702 - Statistical Machine Learning, Spring 2015 - CMU
  • Information Theory, Pattern Recognition, and Neural Networks, 2012 - Cambridge
  • CS189/281A - Introduction to Machine Learning, Spring 2016 - UC Berkeley
  • C281B - Scalable Machine Learning, 2012 - UC Berkeley
  • STA4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto
  • 18.409 - Algorithmic Aspects of Machine Learning, Spring 2015 - MIT
  • 9.520 - Statistical Learning Theory and Applications, Fall 2015 - MIT
  • CPSC530 - Undergraduate Machine Learning, 2012 - University of British Columbia
  • CPSC540 - Graduate Machine Learning, 2013 - University of British Columbia

• Deep Learning

  • CS230 - Deep Learning, Fall 2018 - Stanford
  • 6.S191 - Introduction to Deep Learning - MIT
  • Machine Learning, Fall 2014 - University of Oxford
  • CSC321 - Neural Networks for Machine Learning - University of Toronto
  • MOOC - Deep Learning Specialisation- deeplearning.ai
  • CS231N - Convolutional Neural Networks for Visual Recognition, Spring 2017 - Stanford
  • CS224N - Natural Language Processing with Deep Learning, Winter 2019 - Stanford
  • CS224U - Natural Language Understanding, Spring 2019 - Stanford
  • Deep Learning for Natural Language Processing - Oxford
  • 6.S094 - Deep Learning for Self-Driving Cars - MIT
  • CS294-129 - Designing, Visualizing and Understanding Deep Neural Networks, Fall 2016 - UC Berkeley
  • CS330 - Deep Multi-Task Learning and Meta Learning, Winter 2019 - Stanford
  • CS294-158 - Deep Unsupervised Learning, Spring 2020 - UC Berkeley

• Reinforcement Learning

  • CS294 - Deep Reinforcement Learning, Fall 2018 - UC Berkeley
  • COMPM050 - Reinforcement Learning, 2015 - UCL
  • CS885 - Reinforcement Learning, Spring 2018 - University of Waterloo
  • Advanced Deep Learning & Reinforcement Learning - DeepMind & UCL
  • CS294-112 - Deep Reinforcement Learning, Fall 2018 - UC Berkeley
  • CS234 - Reinforcement Learning, Winter 2019 - Stanford

• Probabilistic Graphical Models

  • 10-708 - Probabilistic Graphical Models, Spring 2014 - CMU
  • CS228 - Probabilistic Graphical Models - Stanford

• Miscs

  • CS246 - Mining of Massive Datasets - Stanford
  • MOOC - Data Mining - University of Illinois
  • MOOC - Recommender Systems - University of Minnesota
  • Information Retrival, Fall 2017 - University of Freiburg
  • Information Retrieval and Web Search Engines, Winter 2015 - Technische Universität Braunschweig
  • CS224W - Machine Learning with Graphs, Fall 2019 - Stanford
  • CS520 - Knowledge Graphs Seminar, Spring 2020 - Stanford