SubjectsSubjects(version: 908)
Course, academic year 2022/2023
  
Machine Learning in Python - M445024
Title: Strojové učení v Pythonu
Guaranteed by: Department of Computing and Control Engineering (445)
Actual: from 2021
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
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.
Annotation -
Last update: Hrnčiřík Pavel doc. Ing. Ph.D. (17.06.2020)
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.
Aim of the course -
Last update: Hrnčiřík Pavel doc. Ing. Ph.D. (17.06.2020)

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.

Literature -
Last update: Hrnčiřík Pavel doc. Ing. Ph.D. (17.06.2020)

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.

Learning resources -
Last update: Hrnčiřík Pavel doc. Ing. Ph.D. (17.06.2020)

Electronic teaching materials for the course.

Syllabus -
Last update: Hrnčiřík Pavel doc. Ing. Ph.D. (17.06.2020)

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.

Entry requirements -
Last update: Hrnčiřík Pavel doc. Ing. Ph.D. (17.06.2020)

Basic knowledge of mathematical statistics and programming in Python.

Registration requirements -
Last update: Hrnčiřík Pavel doc. Ing. Ph.D. (17.06.2020)

Introduction to Python

Course completion requirements - Czech
Last update: Hrnčiřík Pavel doc. Ing. Ph.D. (17.06.2020)

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

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
 
VŠCHT Praha