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Required textbooks
Used copies of the books below should be easily found on Amazon, but all but the first book (ML) are available online for free, see the links in blue.
Machine Learning: M1
by Tom Mitchell (1997) ... Yes, it is "old" but it gives a lot of intuition for the key ideas in machine learning that are important today, and is somewhat less heavy on the math.
A Course in Machine Learning M2
by Hal Daumé III (2017)
Neural Networks and Deep Learning: DL
Michael A. Nielsen, (2015)
Other good textbooks
Deep Learning with Python
2nd Edition (2021) by Francois Chollet
Deep Learning
by Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016)
Machine Learning: An Applied Mathematics Introduction
by Paul Wilmott (2020)
Probabilistic Machine Learning: An Introduction
by Kevin Patrick Murphy (2022)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
2nd Edition (2019) by Aurelien Geron
Pattern Recognition and Machine Learning
by Christopher M. Bishop (2018)
Information Theory, Inference, and Learning
by David MacKay (2003)
Reinforcement Learning: An Introduction: RL
2nd Edition (2018) by Richard S. Sutton, Andrew G. Barto
Your python3 environment
Tutorials and libraries we will use:
Machine Learning Resources
ML reference
ML course (Utah)
ML course (UPenn)
ML course (Alberta)
ML course (IIT Madras)
Deep Learning course (IIT Madras)
Reinforcement Learning course (IIT Madras)
Probability course (UMASS)
Probability course (MIT)
Linear algebra course (MIT)
Linear algebra course (IIT Madras)
Linear algebra review (Stanford)
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