Statistical Machine Learning

The course is not on the list Without time-table
Code Completion Credits Range Language
B4M33SSU Z,ZK 6 2p+2c Czech
Safety in Electrical Engineering for a master´s degree (BEZM)
Boris Flach (guarantor)
Boris Flach (guarantor)
Department of Cybernetics

The aim of statistical machine learning is to develop systems (models and algorithms) able to learn to solve tasks given a set of examples and some prior knowledge about the task. This includes typical tasks in speech and image recognition. The course has the following two main objectives

1. to present fundamental learning concepts such as risk minimisation, maximum likelihood estimation and Bayesian learning including their theoretical aspects,

2. to consider important state-of-the-art models for classification and regression and to show how they can be learned by those concepts.


Prerequisites of the course are:

- foundations of probability theory and statistics comparable to the scope of the course „Probability, statistics and information theory“ (A0B01PSI),

- knowledge of statistical decision theory foundations, canonical and advanced classifiers as well as basics of machine learning comparable to the scope of the course „Pattern Recognition and Machine Learning“ (AE4B33RPZ)

Syllabus of lectures:

The course will cover the following topics

- Empirical risk minimization, consistency, bounds

- Kernel SVMs, RKHS, regression

- Semi-supervised learning

- Unsupervised learning, EM algorithm, mixture models

- Bayesian learning

- Deep (convolutional) networks and Boltzmann machines (graphical models)

- Supervised learning for deep networks

- Hopfield nets and energy minimisation (MAP in MRFs)

- Structured output SVMs

- Sampling methods, sampling from models

- Ensemble learning, random forests

Syllabus of tutorials:

Labs will be dedicated to practical implementations of selected methods discussed in the course as well as seminar classes with task-oriented assignments.

Study Objective:
Study materials:

1. M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012

2. K.P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012

3. T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010

Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2019-03-21
For updated information see http://bilakniha.cvut.cz/en/predmet4684206.html