Statistical Machine Learning
Code  Completion  Credits  Range  Language 

B4M33SSU  Z,ZK  6  2P+2C  Czech 
 Corequisite:
 Lecturer:
 Tutor:
 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 stateoftheart 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
 Semisupervised 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 taskoriented assignments.
 Study Objective:
 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:
 No timetable has been prepared for this course
 The course is a part of the following study plans: