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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.
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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-MLP Machine Learning in Practice Extent of teaching: 2P+1C

Instructor: Hučín J. Completion: Z,ZK

Department: 18105 Credits: 5 Semester: Z

Annotation:
Applying machine learning methods to real projects in practice involves many other necessary tasks - from understanding the intentions of the client to, ideally, technical implementation. The course guides students through all phases of a project according to the standard CRISP-DM methodology, not only theoretically but also practically. The aim is to experience real data processing and learn how to describe the whole process from exploration to evaluation of the model performance in the form of a clear and understandable report.

Lecture syllabus:

1. Machine learning in the context of Data science projects. CRISP-DM methodology.

2. Basic technologies for data analysis and processing.

3. Data understanding.

4. Statistical inference.

5. Applied Bayesianism.

6. Creating a comprehensible report.

7. Data preparation.

8. Modeling practice and model evaluation.

9. Interpretability of models.

10. Application of SW engineering principles.

11. Use of technologies for Big Data.

12. Limits of statistical methods.

Seminar syllabus:

1. Hands-on experience with selected technologies (pandas, scikit-learn, seaborn, mlflow, ...)

2. Basic exploration and visualization, formulation of findings and recommendations for data cleaning.

3. Practical problem solving using Bayesian reasoning.

4. Report generation tools (Quarto, pretty-jupyter, ...)

5. Real data processing: data transformation and feature extraction, 6. building a reference model and improving it.

Literature:

1. Hastie, T. - Tibshirani, R. - Friedman, J. : The Elements of Statistical Learning, Data Mining, Inference and Prediction. Springer, 2011. ISBN 978-0387848570.

2. Murphy, K. P. : Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). MIT Press, 2012. ISBN 978-0262018029.

Requirements:
BI-ML1/BI-ML2

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

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

NI-TI.2018 Computer Science V Není

Page updated 26. 4. 2024, semester: Z/2020-1, L/2021-2, L/2019-20, L/2022-3, Z/2019-20, L/2020-1, L/2023-4, Z/2022-3, Z/2021-2, Z/2023-4, Z/2024-5, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška

NI-MLP	Machine Learning in Practice			Extent of teaching:	2P+1C
Instructor:	Hučín J.			Completion:	Z,ZK
Department:	18105	Credits:	5	Semester:	Z

1.		Machine learning in the context of Data science projects. CRISP-DM methodology.
2.		Basic technologies for data analysis and processing.
3.		Data understanding.
4.		Statistical inference.
5.		Applied Bayesianism.
6.		Creating a comprehensible report.
7.		Data preparation.
8.		Modeling practice and model evaluation.
9.		Interpretability of models.
10.		Application of SW engineering principles.
11.		Use of technologies for Big Data.
12.		Limits of statistical methods.

1.		Hands-on experience with selected technologies (pandas, scikit-learn, seaborn, mlflow, ...)
2.		Basic exploration and visualization, formulation of findings and recommendations for data cleaning.
3.		Practical problem solving using Bayesian reasoning.
4.		Report generation tools (Quarto, pretty-jupyter, ...)
5.		Real data processing: data transformation and feature extraction, 6. building a reference model and improving it.

1.		Hastie, T. - Tibshirani, R. - Friedman, J. : The Elements of Statistical Learning, Data Mining, Inference and Prediction. Springer, 2011. ISBN 978-0387848570.
2.		Murphy, K. P. : Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series). MIT Press, 2012. ISBN 978-0262018029.