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

Web page:

https://cw.fel.cvut.cz/wiki/courses/BE4M33SSU

Anotation:

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.

Study targets:

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.

Course outlines:

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

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

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)

Keywords:

machine learing, statistical learning

Subject is included into these academic programs:

Program	Branch	Role	Recommended semester
MPOI7_2018	Artificial Intelligence	PO	1
MPOI5_2018	Computer Vision and Image Processing	PO	1
MPBIO1_2018	Bioinformatics	PS	1
MPOI9_2018	Data Science	PO	3
MEKYR_2021	Common courses	PV	1,3
MEOI8_2018	Bioinformatics	PO	3
MPBIO4_2018	Signal processing	PV	1
MEBIO3_2018	Image Processing	PS	1
MPKYR_2021	Common courses	PV	1,3
MPOI8_2018	Bioinformatics	PO	3
MPBIO2_2018	Medical Instrumentation	PV	1
MEOI7_2018	Artificial Intelligence	PO	1
MEBIO1_2018	Bioinformatics	PS	1
MPBIO3_2018	Image processing	PS	1
MEOI5_2018	Computer Vision and Image Processing	PO	1
MEBIO2_2018	Medical Instrumentation	PV	1
MEBIO4_2018	Signal Processing	PV	1
MEOI9_2018	Data Science	PO	3

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Page updated 16.4.2024 17:53:17, semester: Z,L/2023-4, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs

Proposal and Realization: I. Halaška (K336), J. Novák (K336)