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:
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:
Automated ML:
Parameter Optimization:
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:
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.