 CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2022/2023
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

# Mathematics for data science

The course is not on the list Without time-table
Code Completion Credits Range Language
NI-MZI 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 MI-MPI: 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, best-subset selection, etc.

7) Singular value decomposition and its connection with ridge regression.

8)  Principal component analysis and dimensionality reduction.

10) Linear regression and classification.

11) Logistic regression.

12) Local regression and smoothing methods (splines, kernels).

13)  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, Springer-Verlag New York (2006), ISBN 978-0-387-31073-2

2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer (2011), ISBN 978-0387848570.

Note:
Further information:
https://courses.fit.cvut.cz/MI-MZI/
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2023-03-18
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