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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024

Statistical Machine Learning

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Code Completion Credits Range Language
BE4M33SSU Z,ZK 6 2P+2C English

In order to register for the course BE4M33SSU, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.

Garant předmětu:
Boris Flach
Lecturer:
Jan Drchal, Boris Flach, Vojtěch Franc
Tutor:
Jan Drchal, Boris Flach, Vojtěch Franc, Jakub Paplhám
Supervisor:
Department of Cybernetics
Synopsis:

The aim of statistical machine learning is to develop systems (models and algorithms) for learning 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.

Requirements:

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

- Maximum Likelihood estimators and their properties

- Unsupervised learning, EM algorithm, mixture models

- Bayesian learning

- Deep (convolutional) networks

- Supervised learning for deep networks

- Hidden Markov models

- Structured output SVMs

- 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:

The aim of statistical machine learning is to develop systems (models and algorithms) for learning to solve tasks given a set of examples and some prior knowledge about the task.

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

4. I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016

Note:
Further information:
https://cw.fel.cvut.cz/wiki/courses/BE4M33SSU
Time-table for winter semester 2023/2024:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
Tue
roomKN:E-107
Flach B.
Franc V.

12:45–14:15
(lecture parallel1)
Karlovo nám.
Zengerova posluchárna K1
roomFS_KN:A-320
Flach B.
Franc V.

12:45–14:15
(lecture parallel1)
Karlovo nám.
Posluchárna FS A:320 - záp.
Wed
Thu
roomKN:E-112
Franc V.
Flach B.

09:15–10:45
(lecture parallel1
parallel nr.103)

Karlovo nám.
Cvičebna Vyčichlova
roomKN:E-112
Franc V.
Flach B.

11:00–12:30
(lecture parallel1
parallel nr.101)

Karlovo nám.
Cvičebna Vyčichlova
roomKN:E-112
Paplhám J.
12:45–14:15
(lecture parallel1
parallel nr.102)

Karlovo nám.
Cvičebna Vyčichlova
Fri
Time-table for summer semester 2023/2024:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-04-23
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet4684906.html