Main page | Study Branches/Specializations | Groups of Courses | All Courses | Roles                Instructions

A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

NI-MZI Mathematics for data science Extent of teaching: 2P+1C
Instructor: Completion: Z,ZK
Department: 18105 Credits: 4 Semester: L

Annotation:
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.

Lecture syllabus:
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) [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.

Seminar syllabus:
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.

Literature:
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.

Requirements:
Knowledge of basic notions of linear algebra and matrix theory, basics of probability theory, course MI-MPI: Mathematics for informatics.

Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/MI-MZI/

The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester
NI-PB.2020 Computer Security V Není
NI-ZI.2020 Knowledge Engineering V Není
NI-SPOL.2020 Unspecified Branch/Specialisation of Study V Není
NI-TI.2020 Computer Science V Není
NI-TI.2023 Computer Science V Není
NI-NPVS.2020 Design and Programming of Embedded Systems V Není
NI-PSS.2020 Computer Systems and Networks V Není
NI-MI.2020 Managerial Informatics V Není
NI-SI.2020 Software Engineering (in Czech) V Není
NI-SP.2020 System Programming V Není
NI-WI.2020 Web Engineering V Není
NI-SP.2023 System Programming V Není


Page updated 29. 3. 2024, semester: L/2021-2, Z,L/2023-4, Z/2021-2, Z/2020-1, Z/2019-20, L/2020-1, Z,L/2022-3, L/2019-20, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška