yngz / Cs Roadmap
My Computer Science Curriculum
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Computer Science Roadmap (AI Track)
Intro to CS
- [x] CS50 - Introduction to Computer Science - Harvard
- [x] 6.0001 - Introduction to Computer Science and Programming in Python, Fall 2016 - MIT
- [x] 6.0002 - Introduction to Computational Thinking and Data Science, Fall 2016 - MIT
Programming
- [x] CS61A - Structure and Interpretation of Computer Programs (Python + Scheme) - UC Berkeley
- [ ] CS61A - Structure and Interpretation of Computer Programs (Scheme), 2010 - UC Berkeley
- [x] 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
-
Linear Algebra
-
Probability and Statistics
- [x] 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
-
Opmitisation
-
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
- [x] MOOC - Discrete Optimization - University of Melbourne
- [ ] Operations Research - SUNY Binghamton University
- [ ] Linear Programming, Fall 2016 - Penn State University
Data Structures and Algorithms
- [x] 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
- [x] 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
-
Machine Learning
- [ ] STATS216 - Statistical Learning, Winter 2016 - Stanford
- [x] 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
- [x] CS231N - Convolutional Neural Networks for Visual Recognition, Spring 2017 - Stanford
- [x] 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
-
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
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