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.
BIE-ML1.21 Machine Learning 1 Extent of teaching: 2P+2C Instructor: Da Silva Alves R., Vašata D. Completion: Z,ZK Department: 18105 Credits: 5 Semester: Z Annotation:
The goal of this course is to introduce students to the basic methods of machine learning. They get theoretical understanding and practical working knowledge of regression and classification models in the supervised learning scenario and clustering models in the unsupervised scenario. Students will be aware of the relationships between model bias and variance, and know the fundamentals of assessing model quality. Moreover, they learn the basic techniques of data preprocessing and multidimensional data visualization. In practical demonstrations, pandas and scikit libraries in Python will be used.
Lecture syllabus:
1. Introduction and basic concepts of Machine Learning 2. Supervised learning setup, Linear regression - Ordinary least squares 3. Linear regression - geometrical interpretation, numerical issues 4. Ridge regression, bias-variance trade-off 5. Classification setup, Decision trees 6. Ensemble methods (Random forests, Adaboost) 7. K-nearest neighbors for classification and regression 8. Logistic regression 9. Model evaluation, cross-validation 10. Feature selection 11. Unsupervised learning setup, Association rules 12. Hierarchical clustering, the k-means algorithm Seminar syllabus:
1. Introduction, Python and jupyter notebooks 2. Supervised learning setup, Linear regression - Ordinary least squares 3. Linear regression - geometrical interpretation, numerical issues 4. Ridge regression, bias-variance trade-off 5. Classification setup, Decision trees 6. Ensemble methods (Random forests, Adaboost) 7. K-nearest neighbors for classification and regression 8. Logistic regression 9. Model evaluation, cross-validation 10. Feature selection 11. Unsupervised learning setup, Association rules 12. Hierarchical clustering, the k-means algorithm Literature:
1. Deisenroth M. P. : Mathematics for Machine Learning. Cambridge University Press, 2020. ISBN 978-1108455145. 2. Alpaydin E. : Introduction to Machine Learning. MIT Press, 2020. ISBN 978-0262043793. 3. Murphy K. P. : Machine Learning: A Probabilistic Perspective. MIT Press, 2012. ISBN 978-0-262-01802-9. 4. Bishop Ch. M. : Pattern Recognition and Machine Learning. Springer, 2006. ISBN 978-0387-31073-2. 5. Hastie T., Tibshirani R., Friedman J. : The Elements of Statistical Learning. Springer, 2009. ISBN 978-0-387-84857-0. Requirements:
The knowledge of calculus, linear algebra and probability theory is assumed.
All informations and course materials can be fond at https://courses.fit.cvut.cz/BIE-ML1/ The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester BIE-TI.21 Computer Science 2021 PV 5
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