Mathematics for data science
Code  Completion  Credits  Range  Language 

NIMZI  Z,ZK  4  2P+1C  Czech 
 Garant předmětu:
 Lecturer:
 Tutor:
 Supervisor:
 Department of Applied Mathematics
 Synopsis:

In this course, students are introduced to those fields of mathematics that are necessary for understanding standard methods and algorithms used in data science. The studied topics include mainly: linear algebra (matrix factorisations, eigenvalues, diagonalization), continuous optimisation (optimisation with constraints, duality principle, gradient methods) and selected notions from probability theory and statistics.
 Requirements:

Knowledge of basic notions of linear algebra and matrix theory, basics of probability theory, course MIMPI: Mathematics for informatics.
 Syllabus of lectures:

1) Mathematical formulation of regression and classification problem.
2) Geometrical view of linear regression model and least squares method (LS).
3) Computing the LS estimate (QR decomposition of a matrix).
4) Hypothesis tests for linear model, model validation.
5) Variable subset selection: ridge regression, bestsubset selection, etc.
7) Singular value decomposition and its connection with ridge regression.
8) [2] Principal component analysis and dimensionality reduction.
10) Linear regression and classification.
11) Logistic regression.
12) Local regression and smoothing methods (splines, kernels).
13) [2] Support vector machines.
 Syllabus of tutorials:

1) Least squares method.
2) Matrix factorisation and matrix eigenvalues.
3) Usage of linear regression and related methods.
4) Principal component analysis.
5) Logistic regression.
6) Support vector machines.
 Study Objective:
 Study materials:

1. Christopher Bishop, Pattern Recognition and Machine Learning, SpringerVerlag New York (2006), ISBN 9780387310732
2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer (2011), ISBN 9780387848570.
 Note:
 Further information:
 https://courses.fit.cvut.cz/MIMZI/
 No timetable has been prepared for this course
 The course is a part of the following study plans:

 Master specialization Computer Security, in Czech, 2020 (elective course)
 Master specialization Design and Programming of Embedded Systems, in Czech, 2020 (elective course)
 Master specialization Computer Systems and Networks, in Czech, 202 (elective course)
 Master specialization Management Informatics, in Czech, 2020 (elective course)
 Master specialization Software Engineering, in Czech, 2020 (elective course)
 Master specialization System Programming, in Czech, version from 2020 (elective course)
 Master specialization Web Engineering, in Czech, 2020 (elective course)
 Master specialization Knowledge Engineering, in Czech, 2020 (elective course)
 Master specialization Computer Science, in Czech, 2020 (elective course)
 Mgr. programme, for the phase of study without specialisation, ver. for 2020 and higher (elective course)
 Study plan for Ukrainian refugees (elective course)