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Machine Learning in Python - M445024

Title:	Strojové učení v Pythonu
Guaranteed by:	Department of Mathematics, Informatics and Cybernetics (446)
Faculty:	Faculty of Chemical Engineering
Actual:	from 2021 to 2022
Semester:	winter
Points:	winter s.:5
E-Credits:	winter s.:5
Examination process:	winter s.:
Hours per week, examination:	winter s.:2/2, C+Ex [HT]
Capacity:	unlimited / unlimited (unknown)
Min. number of students:	unlimited
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time
Level:
For type:	Master's (post-Bachelor)
Note:	course can be enrolled in outside the study plan enabled for web enrollment

Guarantor:	Hrnčiřík Pavel doc. Ing. Ph.D.

Examination dates

Annotation -

The course Machine Learning in Python deals with the practical use of the Python programming language in the field of machine learning. Students will get acquainted with current machine learning algorithms and with the modern Tensorflow and Keras platform. Emphasis is placed on the use of machine learning methods in chemistry and on the ability of students to apply these methods for the solution of practical problems.

Last update: Hrnčiřík Pavel (17.06.2020)

Aim of the course -

Students will be able to:

Create predictions and robust models based on data.

Classify data.

Choose a suitable model and method for solving the problem.

Work with Keras and Tensorflow frameworks.

Validate models and evaluate their accuracy.

Last update: Hrnčiřík Pavel (17.06.2020)

Literature -

Z: François CHOLLET, Deep learning v jazyku Python: knihovny Keras, Tensorflow, Grada Publishing, Praha, 2019.

D: Sebastian RASCHKA, Vahid MIRJALILI, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, Packt Publishing, Birmingham, 2019.

Last update: Hrnčiřík Pavel (17.06.2020)

Learning resources -

Electronic teaching materials for the course.

Last update: Hrnčiřík Pavel (17.06.2020)

Syllabus -

1 Data normalization, transformation, coding.

2 Nonlinear regression analysis.

3 Support vector machine (SVM).

4 Gradient methods.

5 Selected structures of neural networks (MLP, RBF).

6 Self-organizing maps.

7 Project.

8 Nearest Neighbors Algorithm.

9 Cluster analysis.

10 Decision trees.

11 Validation, evaluation of classification accuracy, feature extraction.

12 Convolutional neural networks.

13 Recurrent neural networks.

14 Use of Tensorflow framework in chemistry.

Last update: Hrnčiřík Pavel (17.06.2020)

Entry requirements -

Basic knowledge of mathematical statistics and programming in Python.

Last update: Hrnčiřík Pavel (17.06.2020)

Registration requirements -

Introduction to Python

Last update: Hrnčiřík Pavel (17.06.2020)

Course completion requirements - Czech

Splnění průběžných a zápočtových testů, obhajoba individuálního projektu, zkouškový test.

Last update: Hrnčiřík Pavel (17.06.2020)

Teaching methods
	Activity	Credits	Hours
	Účast na přednáškách	1	28
	Příprava na přednášky, semináře, laboratoře, exkurzi nebo praxi	1.1	30
	Práce na individuálním projektu	1.4	40
	Příprava na zkoušku a její absolvování	0.5	14
	Účast na seminářích	1	28
		5 / 5	140 / 140