An introduction to machine learning.

• Course Jupyter Book

2024 Instructor: Mahmood Amintoosi

I should mention that the original material of this course was from Open Machine Learning Course, by Joaquin Vanschoren and others.

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron. Code/notebooks available here. (Endorsed by an MDS student!)
• James, Gareth; Witten, Daniela; Hastie, Trevor; and Tibshirani, Robert. An Introduction to Statistical Learning: with Applications in R. 2014. Plus Python code and more Python code.
• Russell, Stuart, and Peter Norvig. Artificial intelligence: a modern approach. 1995.
• David Poole and Alan Mackwordth. Artificial Intelligence: foundations of computational agents. 2nd edition (2017). Free e-book.
• Kevin Murphy. Machine Learning: A Probabilistic Perspective. 2012.
• Christopher Bishop. Pattern Recognition and Machine Learning. 2007.
• Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. 2005.
• Mining of Massive Datasets. Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman. 2nd ed, 2014.

• Mathematics for Machine Learning
• The Matrix Calculus You Need For Deep Learning
• Introduction to Optimizers

• A Course in Machine Learning
• Nando de Freitas lecture videos and online course

• Diabetic retinopathy Kaggle competition write-up
• Galaxy Zoo Kaggle competition write-up
• National Data Science Bowl competition write-up

• jupyter-book build ./
• ghp-import -n -p -f ./_build/html