Mathematics for data science
Code  Completion  Credits  Range  Language 

MIMZI  Z,ZK  4  2P+1C  Czech 
 Lecturer:
 Daniel Vašata, Štěpán Starosta (guarantor), Karel Klouda
 Tutor:
 Daniel Vašata, Štěpán Starosta (guarantor), Karel Klouda
 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:
 Timetable for winter semester 2019/2020:
 Timetable is not available yet
 Timetable for summer semester 2019/2020:

06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Fri Thu Fri  The course is a part of the following study plans:

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 Master Informatics, Presented in Czech, Version for Students who Enrolled in 2015 (elective course)
 Knowledge Engineering, in Czech, Presented in Czech, Version 2016 and and 2017 (elective course)
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 Specialization Software Engineering, in Czech, Version 2016 to 2019 (elective course)
 Specialization Web Engineering, Presented in Czech, Version 2016 to 2019 (elective course)
 Master Informatics, Presented in Czech, Version 2016 to 2019 (elective course)
 Specialization System Programming, Presented in Czech, Version 2016 to 2019 (elective course)
 Specialization Computer Science, Presented in Czech, Version 20162017 (elective course)
 Specialization Computer Science, Presented in Czech, Version 2018 to 2019 (elective course)
 Knowledge Engineering, in Czech, Presented in Czech, Version 2018 to 2019 (elective course)