Kernel Density estimation. EM-algorithm for mixture of Gaussians.
Dimensionality reduction using PCA and Kernel-PCA.
Other Topics: Overview of Reinforcement Learning. Bias and Ethics in ML.
Optional topics: Introduction to one or more of the following topics: Active/Transfer Learning, Bootstrapping, Semi-supervised learning, Generative and probabilistic graphical models, Online/incremental learning.
Pattern Recognition and MachineLearning, by Christopher Bishop,Springer 2011
The Elements of Statistical Learning:Data Mining, Inference, and Prediction,Second Edition, by Trevor Hastie andRobert Tibshirani (Springer Series inStatistics) 2016
Supplementary material available online,e.g. Dive into Deep Learning by AstonZhang, Zack C. Lipton, Mu Li andAlexander Smola, 2020 (https://d2l.ai)
6 credits - Lecture
Name(s) of other Academic units to whom the course may be relevant