Machine Learning
- Credits: 6
- Ending: Examination
- Range: 2P + 2C
- Semester: winter
- Year: 2
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Successful completion of the course is a guarantee that students will gain a basic overview of the nature and possibilities of machine learning in practice.
Knowledge
Students acquire:
− knowledge of basic concepts, principles, methods and procedures used in machine learning,
− knowledge of Python programming language
Skills
− students will learn to implement statistical methods into codes
− students will be able to construct machine learning models and algorithms in the Python programming language and will know how to combine them in solving problems
− students will learn to adequately apply machine learning procedures and methods
− students will learn to use libraries in Python, including the popular Scikit-learn and TensorFlow for machine learning
Competences
− students will be able to use the acquired knowledge and skills in solving tasks of machine learning
Indicative content
The subject represents the area of machine learning, which is currently being intensively developed in close connection with artificial intelligence. It gives an overview of the basic types of machine learning, the main problems and methods and lists some typical algorithms.
Support literature
1. MŰLLER, A. C., & GUIDO, S. (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists (1st ed.). O’Reilly Media. ISBN 978-1-449-36941-5
GÉRON, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed.). O’Reilly Media. ISBN 978-1492032649
2. AMR, T. (2020). Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python. Packt Publishing.
3. ALBON, C. (2018). Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning (1st ed.). O’Reilly Media. ISBN 978-1491989388
4. LIU, Y. (2020). Python Machine Learning By Example: Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn (3rd ed.). Packt Publishing. ISBN 978-1800209718
Syllabus
1. Introduction to machine learning and Python 2. Data preparation and data cleaning 3. Training, validation, and test sets 4. Classification a Regression 5. K-Nearest Neighbor 6. Random Forest and Decision Trees 7. Support Vector Machine algorithm 8. Naïve Bayes algorithm 9. Unsupervised learning. Clustering – K means clustering 10. Artificial Neural Networks I 11. Artificial Neural Networks II 12. Model validation. Model quality evaluation criteria. 13. Summary
Requirements to complete the course
40% assignment in Python
60% final exam
Student workload
Total study load (in hours): 156
Lecture participation: 26
Seminar participation: 26
Preparation for seminars: 26
Written assignments: 38
Final exam preparation: 40
Language whose command is required to complete the course
Slovak
Date of approval: 10.02.2023
Date of the latest change: 02.02.2022