Statistical Machine Learning

Vztahy:

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:

Vojtěch Franc

Lecturer:

Jan Drchal, Vojtěch Franc

Tutor:

Jan Drchal, 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 2024/2025:

	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 Franc V. Drchal J. 12:45–14:15 (lecture parallel1) Karlovo nám. Zengerova posluchárna K1
Wed
Thu	roomKN:E-112 09:15–10:45 (lecture parallel1 parallel nr.103) Karlovo nám. Cvičebna VyčichlovaroomKN:E-112 11:00–12:30 (lecture parallel1 parallel nr.101) Karlovo nám. Cvičebna VyčichlovaroomKN:E-112 12:45–14:15 (lecture parallel1 parallel nr.102) Karlovo nám. Cvičebna Vyčichlova
Fri

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans:

• Medical electronics and bioinformatics (PS)
• Open Informatics - Computer Vision and Image Processing (compulsory course of the specialization)
• Open Informatics - Artificial Intelligence (compulsory course of the specialization)
• Open Informatics - Bioinformatics (compulsory course of the specialization)
• Open Informatics - Data Science (compulsory course of the specialization)
• Open Informatics - Artificial Intelligence (compulsory course of the specialization)
• Open Informatics - Computer Vision and Image Processing (compulsory course of the specialization)
• Open Informatics - Bioinformatics (compulsory course of the specialization)
• Open Informatics - Data Science (compulsory course of the specialization)
• Medical electronics and bioinformatics (compulsory elective course)
• Medical electronics and bioinformatics (PS)
• Medical electronics and bioinformatics (compulsory elective course)
• Medical Electronics and Bioinformatics - Specialization Image Processing (PS)
• Medical Electronics and Bioinformatics - Specialization Signal Processing (compulsory elective course)
• Medical Electronics and Bioinformatics - Specialization Bioinformatics (PS)
• Medical Electronics and Bioinformatics - Specialization Medical Instrumentation (compulsory elective course)
• Cybernetics and Robotics (compulsory elective course)
• Cybernetics and Robotics (compulsory elective course)