Mathematics for data science
Code  Completion  Credits  Range  Language 

MIMZI  Z,ZK  4  2P+1C  Czech 
 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 branch Knowledge Engineering, in Czech, 20162017 (elective course)
 Master branch Computer Security, in Czech, 20162019 (elective course)
 Master branch Computer Systems and Networks, in Czech, 20162019 (elective course)
 Master branch Design and Programming of Embedded Systems, in Czech, 20162019 (elective course)
 Master branch Web and Software Engineering, spec. Info. Systems and Management, in Czech, 20162019 (elective course)
 Master branch Web and Software Engineering, spec. Software Engineering, in Czech, 20162019 (elective course)
 Master branch Web and Software Engineering, spec. Web Engineering, in Czech, 20162019 (elective course)
 Master program Informatics, unspecified branch, in Czech, version 20162019 (elective course)
 Master branch System Programming, spec. System Programming, in Czech, 20162019 (elective course)
 Master branch System Programming, spec. Computer Science, in Czech, 20162017 (elective course)
 Master specialization Computer Science, in Czech, 20182019 (elective course)
 Master branch Knowledge Engineering, in Czech, 20182019 (elective course)