Machine learning

Marta Vomlelov√°


Slides Slides the last slide - list of topics - is also the list of topics for the exam.
Zoom recordings 2001 Zoom Recordings.
Consultations ask by e-mail Marta.Vomlelova[at]mff.cuni.cz
Contact E-mail Marta.Vomlelova[at]mff.cuni.cz
Key book [ESLII] The Elements of Statistical Learning.
Czech version with English slides is on moodle.


2021 Lectures (reordered in 2022)

1.

Introduction, linear regression, k-NN, expected prediction error, Curse of dimensionality

Section 2 in ESLII

2.

Linear regression, Ridge, Lasso regression, Undirected Graphical Models (first part)

Section 17.1-17.3,17.4.4 in ESLII - later editions, like the internet one.
Further sources: S. Hojsgaard, D. Edwards, S. Lauritzen: Graphical Models with R, Springer 2012,
3.

Undirected Graphical Models (second part), Gaussian Processes

C. E. Rasmussen \& C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006
Peter I. Frazier: A Tutorial on Bayesian Optimization, 2018
4.

Splines (Basis Expansion and Regularization)

Sections 5 and 6 ESLII
5.

Linear Models for Classification

Section 4 ESLII
6.

Model Assesment and Selection

Sections 7 ESLII
7.

Decision Trees and Related Methods (MARS)

Sections 9 ESLII
8.

Model Inference and Averaging

Sections 8,10,15,16 ESLII
9.

Clustering

Selected parts of Chapter 14 ESLII, mean shift clustering and Silhouette from Scikitlearn
10.

Bayesian Learning, Other use of the EM algorithm

11.

Association rules, Frequent itemsets (Apriori algorithm)

12.

Support Vector Machines (+ Independent Component Analysis)

13.

Inductive Logic Programming



Sylaus:


Introductory book:

Knihu Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani: An Introduction to Statistical Learning with Applications in R .