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Machine Learning & Deep Learning Resources Compilation

Compilation of resources found around the web connected with Machine Learning, Deep Learning & Data Science in general.

Academic Machine Learning:

• Oxford Machine Learning, 2014-2015 Slides in .pdf, videos. Mathematical problem sets & practicals in Torch. By Nando de Freitas.
• NYU DS-GA-1003: Machine Learning and Computational Statistics, Spring 2016 Slides, notes, additional references to books and videos for some of the lectures.
• CMU 10-701/15-781 Machine Learning, Spring 2011 Lectures by Tom Mitchell. Slides, videos, additional readings and handouts.
• CMU 10-701/15-781 Machine Learning, 2015 Lectures by Alex Smola. Slides, high-quality videos, additional readings and handouts.
• Stanford CS229: Machine Learning A classic by Andrew NG. Video lectures (old but very good in terms of content!), useful notes & review materials + assignmets. Materials (except videos) from 2016 available here.
• Columbia COMS 4771: Machine Learning & COMS 4772: Advanced Machine Learning Lecture notes in form of slides + related notes and homework assignments.
• Berkeley CS 189/289A: Introduction to Machine Learning, Spring 2017 Lecture notes and assigments.
• UBC CPSC 340: Machine Learning and Data Mining, 2012 Bachelor's level ML course by Nando de Freitas. Videos, slides and assignments.
• UBC CPSC 540: Machine Learning, 2013 MSc level course analogous to the one above by Nando de Freitas. Videos, slides and assignments.
• Duke STA561 COMPSCI571: Probabilistic Machine Learning, Fall 2015 Notes and readings + homeworks.

Advanced & Theoretical ML:

• CMU 10-715: Advanced Introduction to Machine Learning, Fall 2015 Video lectures by Alex Smola & Barnabas Poczos, slides and additional readings + homework.
• CMU 10-702/36-702: Statistical Machine Learning, Spring 2016 Lecture videos, notes and assignments by Larry Wasserman. Cource concentrated on theoretical foundations.
• Harvard CS281: Advanced Machine Learning, Fall 2013 Compiled resources on topics contained in the course - videos, papers, notes and assignments.
• John Hopkins University: Unsupervised Learning: From Big Data to Low-Dimensional Representations, 2017 Video lectures and book.
• Princeton COS511: Theoretical Machine Learning, Spring 2014 Lecture notes and readings.
• University of Washington EE512A: Advanced Inference in Graphical Models, Fall Quarter, 2014 Lecture videos & slides.
• Berkeley CS281a: Statistical Learning Theory Metacademy roadmap wit various materials on topics connected with the course.
• MIT 9.520/6.860: Statistical Learning Theory and Applications, Fall 2016 Readings & link to videos from Fall 2015 class.

Academic Deep Learning:

• Stanford CS231n: Convolutional Neural Networks for Visual Recognition Video lectures, notes, papers and coding assignments in python.
• Stanford CS224d: Deep Learning for Natural Language Processing Video lectures, notes, papers and problem sets.
• Toronto CSC2523: Deep Learning in Computer Vision A lot of papers & some code connected with DL in CV.
• Berkeley Stat212B: Topics Course on Deep Learning, Spring 2016 Lecture slides and a lot of papers to read.

Reinforcement Learning:

• UCL Course on RL, 2015 Course on RL by David Silver. Video lectures and slides.
• Berkeley CS294: Deep Reinforcement Learning, Spring 2017 Lecture videos, slides, papers and additional resources.

Various very useful ML & theoretical resources:

• Awesome Machine Learning
• Awesome courses - Machine Learning
• Awesome courses - Deep Learning
• Awesome Natual Language Processing
• All awesome lists
• Machine Learning Salon - compilation of resources by Jacqueline Isabelle Forien
• Reddit PhD-level ML courses
• 35 Free Online Books on Machine Learning
• Geekbooks - a lot of free (older than 2016) and paid (3$/month, newer) great books on various IT topics

Mathematics:

• Awesome math
• Oxford Statistics
• CMU 36-705 Intermediate Statistics by Larry Wasserman, advanced theoretical course
• NYU DS-GA 1002: Statistical and Mathematical Methods
• Stanford EE364a Convex Optimization I, 2016-17. Videos (older), textbook & slides
• Harvard CS109 Data Science
• MIT Introduction to Probability and Statistics, Spring 2014
• MIT Probabilistic Systems Analysis and Applied Probability, Fall 2010. Great course on probability - a slightly differnet version available on edX
• MIT Mathematics for Computer Science, Fall 2010
• Recommended Math books - various topics

Practical resources:

Deep Learning libraries-related compilations:

• Awesome Mxnet
• The Incredible PyTorch
• Keras resources
• Nervana neon

Tensorflow (chosen few):

• Tensorflow tutorials - Hvass Labs, github & youtube videos
• Deep Learning with TensorFlow - Big Data University, course & youtube videos
• Stanford CS20SI: Tensorflow for Deep Learning Research + Unofficial videos
• Effective Tensorflow

Other Courses and tutorials:

• ML & DL Tutorials Compilation
• Python Data Science tutorials
• Modern Pandas - 7 parts on pandas code
• Duke Computational Statistics in Python
• fast.ai Practical Deep Learning For Coders - part I and II

Various Python:

ML Libraries:

• CatBoost
• hdbscan Clustering
• Regularized Greedy Forest

Automated ML:

• auto-sklearn
• mlxtend)
• Hyperas - Keras + HyperOpt

Parameter Optimization:

• BayesianOptimization
• HyperOpt

Various:

• fancyimpute - Various NA's imputation methods
• statsmodels - Statistics & Econometrics in Python
• pattern - Web scraping tool

Feature Encoding:

• Feature Enginnering Presentation GOOD!
• Category Encoders library
• CatBoost categorical feature encoding
• Categorical feature encoding tutorial

Other:

• 28 Jupyter Notebook tips & tricks
• Supervised ML Cheathseet
• 43 Google's Rules of Machine Learning

Credits

Big thanks to all contributors to awesome lists (posted in other resources), which enabled me to find some of the courses contained in the list.