Introduction to Machine Learning (I2ML) by LMU Munich

This website offers an open and free introductory course on (supervised) machine learning. The course is constructed as self-contained as possible, and enables self-study through lecture videos, PDF slides, cheatsheets, quizzes, exercises (with solutions), and notebooks.

The quite extensive material can roughly be divided into:
- An introductory undergraduate part (chapters 1-10)
- A more advanced second one on MSc level (chapters 11-19)
- A third course, on MSc level (chapters 20-23).

A key goal of the course is to teach the fundamental building blocks behind ML, instead of introducing “yet another algorithm with yet another name”. We discuss, compare, and contrast risk minimization, statistical parameter estimation, the Bayesian viewpoint, and information theory and demonstrate that all of these are equally valid entry points to ML. Developing the ability to take on and switch between these perspectives is a major goal of this course, and in our opinion not always ideally presented in other courses.

Link:
- Main Course Website

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