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

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
• MOOC - Discrete Optimization

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

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

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
• Machine Learning, Fall 2014 - University of Oxford
• 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

Deep Learning

• CSC321 - Neural Networks for Machine Learning - University of Toronto
• CS230 - Deep Learning - Stanford
• CS231N - Convolutional Neural Networks for Visual Recognition, Spring 2017 - Stanford
• CS224N - Natural Language Processing with Deep Learning, Winter 2017 - Stanford

Reinforcement Learning

• CS294 - Deep Reinforcement Learning, Fall 2018 - UC Berkeley
• COMPM050 - Reinforcement Learning, 2015 - UCL

Probabilistic Graphical Models

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

Data Mining

• CS246 - Mining of Massive Datasets - Stanford
• MOOC - Data Mining - University of Illinois
• CS224W - Social and Information Network Analysis, Fall 2017 - Stanford
• MOOC - Recommender Systems - University of Minnesota

Data Engineering

• MOOC - Big Data for Data Engineering - Yandex
• MOOC - Cloud Computing - University of Illinois
• MOOC - Big Data - UC San Diego