Subject description - BE4M33SSU

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BE4M33SSU Statistical Machine Learning Extent of teaching:2P+2C
Guarantors:Flach B. Roles:PO,PV,V Language of
teaching:
EN
Teachers:Drchal J., Flach B., Franc V. Completion:Z,ZK
Responsible Department:13133 Credits:6 Semester:Z

Anotation:

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.

Study targets:

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.

Course outlines:

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

Exercises outline:

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

Literature:

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

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)

Webpage:

http://cw.fel.cvut.cz/wiki/courses/be4m33ssu/start

Keywords:

machine learing, statistical learning

Subject is included into these academic programs:

Program Branch Role Recommended semester
MEOI7_2018 Artificial Intelligence PO 1
MEOI9_2016 Data Science PO 3
MEOI5_2018 Computer Vision and Image Processing PO 1
MEBIO_2018 Common courses V 1
MPBIO3_2018 Common courses PV 1
MEOI5_2016 Computer Vision and Image Processing PO 1
MPBIO2_2018 Common courses PV 1
MEBIO_2018 Common courses V 1
MPOI8_2018 Bioinformatics PO 3
MPBIO4_2018 Common courses PV 1
MPBIO1_2018 Common courses PV 1
MEOI7_2016 Artificial Intelligence PO 1
MPOI9_2016 Data Science PO 3
MPOI7_2016 Artificial Intelligence PO 1
MEOI8_2016 Bioinformatics PO 3
MPOI5_2018 Computer Vision and Image Processing PO 1
MPOI9_2018 Data Science PO 3
MEBIO_2018 Common courses PV 1
MPOI8_2016 Bioinformatics PO 3
MPOI5_2016 Computer Vision and Image Processing PO 1
MEOI9_2018 Data Science PO 3
MEOI8_2018 Bioinformatics PO 3
MPOI7_2018 Artificial Intelligence PO 1


Page updated 13.12.2019 12:52:19, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)