There are many good references for machine learning. We will cover material in different textbooks, many of which are freely available online. After each lecture, assigned readings from textbooks will be posted in the course schedule.

[TM] Tom Mitchell, Machine Learning (1997)
[GBC] Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning (2016), freely available online
[HTF] Trevor Hastie, Robert Tibshirani and Jerome Friedman, Elements of Statistical Learning (2nd edition, 2009), freely available online
[D] Hal Daume III, A Course in Machine Learning (in progress), freely available online
[B] Christopher Bishop, Pattern Recognition and Machine Learning (2006), freely available online
[RN] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd Edition) (2010)
[M] Kevin Murphy, Machine Learning: A Probabilistic Perspective (2012)
[MRT] Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, Foundations of Machine Learning (2012)
[SSBD] Shai Shalev-Shwartz, Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms (2014) freely available online
[SutBar] Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction (2nd edition, 2018), freely available online.
[Sze] Csaba Szepesvari, Algorithms for Reinforcement Learning, freely available online.
[BS] Burr Settles, Active Learning, freely available online.
[Z] Xiaojin Zhu and Andrew B. Goldberg, Introduction to Semi-supervised Learning, freely available online
[ZLLS] Aston Zhang, Zachary C. Lipton, Mu Li and Alexander J. Smola, Dive in Deep Learning, freely available online
[CT] Sonia Chernova and Andrea Thomaz. Robot Learning from Human Teachers, freely available online
[BHN] Solon Barocas, Moritz Hardt, Arvind Narayanan. Fairness and Machine Learning: Limitations and Opportunities, freely available online

Domain-Specific Textbooks

[JM]Dan Jurafsky and James Martin. Speech and Language Processing, freely available online

Review of Mathematics Relevant To Machine Learning

Linear Algebra Review and Reference (by Zico Kolter)
Review of Probability Theory (by Arian Maleki and Tom Do)
Math for Machine Learning (by Hal Daume III)
Convex Optimization (by Boyd and Vandenberghe).


International Conference on Machine Learning (ICML)
AAAI Meetings
Neural Information Processing Systems (NeurIPS)
Computational Learning Theory (COLT)
Uncertainty in Artificial Intelligence (UAI)
International Conference on Knowledge Discovery and Data Mining (KDD)
IEEE International Conference on Data Mining (ICDM)
European Conference on Machine Learning (ECML)
International Joint Conference on Artificial Intelligence (IJCAI)


UCI Machine Learning Repository (from UC Irvine)
Data World
Enigma Public
Datasets for Diaster Relief
American Sign Language Lexicon Video Dataset (SLLVD)
50 Best Public Datasets for Machine Learning
Open Images V4 (from Google AI)
MURA Bone X-Rays (from Stanford ML Group)
BDD100K Driving Videos Dataset (from Berkeley)
SQuAD 2.0 Question Answering Dataset (from Stanford)
CoQA Conversational Question Answering Dataset (from Stanford)
Hotpot Multi-hop Question Answering Dataset (from CMU)
Tencent Multi-label Image Dataset (from Tencent AI Lab)
FastMRI Dataset (from NYU and Facebook AI)
Hate Speech Detection Dataset
Tweet Classification for Detecting PTSD and Depression
Kaggle Toxic Language Detection Challenge
Environmental Sounds Datasets

Machine Learning and Feature Extraction Toolkits

Open Pose
VL Feat

Other Tools

Google Colabs

Additional Reading

Key Ideas in Machine Learning by Tom Mitchell
A Few Useful Things to Know about Machine Learning by Pedro Domingos
Image Classification Practical by Andrea Vedaldi and Andrew Zisserman.
Fairness in Machine Learning by Moritz Hardt.