Statistical Machine Learning 1RT700 61808 VT2022
This is an introductory course to statistical machine learning for students with some background in calculus, linear algebra, and statistics. The course is focusing on supervised learning, i.e., classification and regression. These methods will be studied and applied to real data from various applications throughout the course. Other important practical considerations that are covered include cross-validation, model selection, and the bias-variance trade-off. In addition to the necessary theory (e.g., derivations and proofs), this course also includes practical sessions (notably the lab and the mini-project). The practical part will be implemented using Python.
The book for this course is Machine Learning - A First Course for Engineers and Scientists. It is available online for free. Certain chapters will be recommended for reading each week. We also link to videos and other material. There is a lot of very good video material on YouTube which can help in your learning. Use it as much as you like!
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