Statistical Machine Learning 1RT700 11808 HT2022
This is an introductory course to statistical machine learning for students with some background in elementary calculus, linear algebra, and probability theory.
The course is focusing on supervised learning, i.e., classification and regression with real data, and its computational and statistical foundations. In addition to the necessary theory (e.g., derivations and proofs), this course also includes practical sessions (notably the lab and the project). The practical part will be implemented using Python.
The recommended course book is Machine Learning - A First Course for Engineers and Scientists, which is available online free of charge. Certain chapters will be recommended for reading each week. Link to videos and other material are also provided below.
The course assumes the students have background in elementary calculus, linear algebra, and probability theory. The following examples are concepts used in the course and it is expected that the students have some familiarity with them:
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