SylabUZ
| Course name | Introduction to machine learning |
| Course ID | 13.2-WF-FizD-IML-S21 |
| Faculty | Faculty of Physics and Astronomy |
| Field of study | Physics |
| Education profile | academic |
| Level of studies | Second-cycle studies leading to MS degree |
| Beginning semester | winter term 2023/2024 |
| Semester | 3 |
| ECTS credits to win | 4 |
| Available in specialities | Computer Physics |
| Course type | obligatory |
| Teaching language | english |
| Author of syllabus |
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| The class form | Hours per semester (full-time) | Hours per week (full-time) | Hours per semester (part-time) | Hours per week (part-time) | Form of assignment |
| Laboratory | 45 | 3 | - | - | Credit with grade |
| Lecture | 15 | 1 | - | - | Exam |
The aim of the course is to familiarize students with the basics of machine learning, its terminology and areas of application, and the ability to use common libraries to solve simple / typical ML problems.
Fundamentals of programming. fundamentals of statistics and data analysis
1. Introduction to machine learning, history, terminology, applications
2. Overview of machine learning libraries available in Python
3. Classifiers
4. Preliminary data processing
5. Data reduction methods
6. Model evaluation methods
Lecture, classes, computer laboratory, discussion.
| Outcome description | Outcome symbols | Methods of verification | The class form |
Laboratorium - pozytywna ocena z kolokwium (50%) i przygotowanie sprawozdania z opracowania wybranego zagadnienia z analizy danych (50%).
Wykład - pozytywna ocena z egzaminu pisemnego.
Ocena końcowa - średnia z ocen z laboratorium i egzaminu.
1. "Python. Uczenie maszynowe. Wydanie II", Sebastian Raschka, Vahid Mirjalili, Helion
2. "Uczenie maszynowe z uzyciem SciKit-Learn i TensorFlow. Wydanie II", Aurélien Géron, Helion
3. "Python. Data Science Handbook", Jake VanderPlas, O'Reilly
Internet
Modified by dr Marcin Kośmider (last modification: 20-06-2023 07:52)
