COS 324 - Introduction to Machine Learning [Princeton]

[mklabgr]

The COS 324: Introduction to Machine Learning notes from Princeton University provide a mathematically grounded introduction to the fundamental ideas of machine learning. The course begins with supervised learning, covering linear regression, statistical learning theory, gradient descent, and linear classification, before moving to unsupervised learning, including clustering, dimensionality reduction, language models based on n-grams, and matrix factorization/recommender systems. The notes were prepared by Sanjeev Arora, Danqi Chen, and Princeton students, and were last updated in September 2024. 

The second half introduces deep learning and reinforcement learning, including neural networks, backpropagation, convolutional neural networks, Markov decision processes, and learning in unknown environments. It also includes advanced topics such as machine-learning ethics and NLP, followed by dedicated mathematical foundations in probability and statistics, calculus, and linear algebra. Overall, the material is particularly useful for someone seeking a rigorous university-level foundation: it connects the mathematical principles behind machine-learning algorithms with modern topics such as deep learning and reinforcement learning, rather than treating machine learning purely as a collection of programming techniques. 

Princeton COS 324 course notes