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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2018/2019

Statistical Machine Learning

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Code Completion Credits Range Language
BE4M33SSU Z,ZK 6 2p+2c
Corequisite:
Safety in Electrical Engineering for a master´s degree (BEEZM)
Lecturer:
Vojtěch Franc, Boris Flach (guarantor), Jan Drchal
Tutor:
Vojtěch Franc, Boris Flach (guarantor), Jan Drchal
Supervisor:
Department of Cybernetics
Synopsis:

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.

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

- 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:

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.

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

Note:
Further information:
http://cw.fel.cvut.cz/wiki/courses/be4m33ssu/start
Time-table for winter semester 2018/2019:
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
Fri
Thu
roomKN:G-205
Drchal J.
09:15–10:45
(lecture parallel1
parallel nr.103)

Karlovo nám.
seminární místnost
roomKN:E-220
Flach B.
11:00–12:30
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
roomKN:G-205
Franc V.
12:45–14:15
(lecture parallel1
parallel nr.102)

Karlovo nám.
seminární místnost
Fri
Time-table for summer semester 2018/2019:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2019-05-23
For updated information see http://bilakniha.cvut.cz/en/predmet4684906.html