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Bootcamp – introduction to mathematics

General data

Course ID:	1000-317bBIM
Erasmus code / ISCED:	11.3 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Bootcamp – introduction to mathematics
Name in Polish:	Obóz wstępny – wprowadzenie do matematyki
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	Obligatory courses for 1st year Machine Learning
ECTS credit allocation (and other scores):	3.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language:	English
Type of course:	elective monographs
Short description:	The goal of the course is to present the set of common mathematical notions necessary to understand contemporary techniques of machine learning as well as to instil the mathematical apparatus necessary to efficiently use them.
Full description:	The lecture has the form of an intensive course taught during the first two weeks of the first semester. The topics are divided into three thematic groups: * Linear algebra and geometry (2 lectures) + SVD decomposition + Other matrix decompositions + Structure theorems * Calculus (2 lectures) + Chain rule + Multivariate integrals * Probability theory and statistics (3 lectures) + Random variables, mean, variance, higher moments + Central Limit Theorem + Typical probability distributions
Bibliography:	1. A. Białynicki-Birula, Algebra liniowa z geometrią, Państwowe Wydawnictwo Naukowe, Biblioteka Matematyczna t.48, Warszawa 1979. 2. Zbiór zadań z algebry , pod red. A. I. Kostrikina, wydanie drugie zmienione, Wydawnictwo Naukowe PWN, 2005-2013 3. T. Koźniewski, Wykłady z algebry liniowej I i II , Uniwersytet Warszawski, 2004, 2006 4. Kazimierz Kuratowski, Rachunek różniczkowy i całkowy. Funkcje jednej zmiennej, PWN. 5. W. Kołodziej, Analiza matematyczna, PWN, Warszawa 2009. 6. A. Birkholc, Analiza matematyczna: Funkcje wielu zmiennych. Wydanie II, PWN, Warszawa 2018. 7. G. M. Fichtenholz, Rachunek różniczkowy i całkowy. Tom 1-3, PWN, Warszawa 2007. 8. W. Rudin, Podstawy analizy matematycznej, PWN, Warszawa 2009. 9. W. Rudin, Analiza rzeczywista i zespolona, PWN, Warszawa 2009. 10. P. Strzelecki, Analiza matematyczna II (skrypt wykładu), http://dydmat.mimuw.edu.pl/sites/default/files/wyklady/analiza-matamatyczna-ii.pdf 11. J. Jakubowski, R. Sztencel, Rachunek prawdopodobieństwa dla prawie każdego, Script, Warszawa 2006. 12. W. Krysicki i współautorzy, Rachunek prawdopodobieństwa i statystyka matematyczna w zadaniach , część I, II, Wydawnictwo Naukowe PWN, Warszawa 2004. 13. W. Feller, Wstęp do rachunku prawdopodobieństwa , Wydawnictwo Naukowe PWN, Warszawa 2006. (dla chętnych)
Learning outcomes:	Knowledge: the student * has in-depth understanding of the branches of mathematics necessary to study machine learning (probability theory, statistics, multivariable calculus, and linear algebra) [K_W05] Abilities: the student is able to * construct mathematical reasoning [K_U06]; * express problems in the language of mathematics [K_U07]. Social competences: the student is ready to * critically evaluate acquired knowledge and information [K_K01]; * recognize the significance of knowledge in solving cognitive and practical problems and the importance of consulting experts when difficulties arise in finding a self-devised solution [K_K02]
Assessment methods and assessment criteria:	Mid-term/end-term test

Classes in period "Winter semester 2024/25" (past)

Time span:	2024-10-01 - 2025-01-26	Selected timetable range: weekly course term Go to timetable MO WYK CW CW TU WYK CW CW W WYK CW CW TH WYK WYK CW CW CW CW FR CW CW
Type of class:	Classes, 15 hours more information Lecture, 15 hours more information
Coordinators:	Andrzej Nagórko
Group instructors:	Gracjan Góral, Andrzej Nagórko, Mateusz Wyszyński
Students list:	(inaccessible to you)
Credit:	Examination

Classes in period "Winter semester 2025/26" (future)

Time span:	2025-10-01 - 2026-01-25	Selected timetable range: weekly course term Go to timetable MO WYK CW CW TU WYK CW CW W TH WYK CW CW FR WYK CW CW
Type of class:	Classes, 15 hours more information Lecture, 15 hours more information
Coordinators:	Andrzej Nagórko
Group instructors:	Kamil Ciebiera, Andrzej Nagórko, Mateusz Wyszyński
Students list:	(inaccessible to you)
Credit:	Course - Examination Lecture - Examination

Course descriptions are protected by copyright.
Copyright by University of Warsaw.