Machine Learning

Język wykładowy Angielski
Semestr Zimowy
Status Poddana pod głosowanie
Opiekun Jan Chorowski
Liczba godzin 30 (wyk.) 30 (ćw-prac.)
Rodzaj I2.Z - zastosowania inf.
Polecany dla I roku No
Egzamin Yes
Tagi PD (przetwarzanie danych) DS (Data Science)

Opis przedmiotu:

_Wyklucza się z przedmiotem Sieci Neuronowe i Deep Learning zaliczonym do 2018/19._ **Informations for enrolled/prospective students**: all information is on SKOS (please self-enroll into [Machine Learning 2020](, materials are on [GitHub](, lectures will in-class, streamed and recorded. This course provides the fundamentals Machine Learning, i.e. of designing programs that implement a data-driven, rather than hand-implemented behavior. The course provides a gentle introduction of the topic, but strives to provide enough details and intuitions to explain state-of-the-art ML approaches: ensembles of Decision Trees (Boosted Trees, Random Forests) and Neural Networks. Starting with simple linear and Bayesian models, we proceed to learn the concepts of trainable models, selecting the best model based on data, practical and theoretical ways of estimating model performance on new data, and the difference between discriminative and generative training. The course introduces mainstream algorithms for classification and regression including linear models, Naive Bayes, trees, ensembles, and matrix factorizations for recommendation systems. Practical sessions provide a hands-on experience with the methods.