26M111APTM - Data Analysis and Machine Learning Techniques
Course specification | ||||
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Course title | Data Analysis and Machine Learning Techniques | |||
Acronym | 26M111APTM | |||
Study programme | Electrical Engineering and Computing | |||
Module | Applied Mathematics, Audio and Video Technologies, Biomedical and Nuclear Engineering, Computer Engineering and Informatics, Electronics and Digital Systems, Energy Efficiency, Information and Communication Technologies, Microwave Engineering, Nanoelectronics and Photonics, Power Systems - Networks and Systems, Power Systems - Renewable Energy Sources, Power Systems - Substations and Power Equipment, Signals and Systems, Software Engineering | |||
Type of study | master academic studies | |||
Lecturer (for classes) | ||||
Lecturer/Associate (for practice) | ||||
Lecturer/Associate (for OTC) | ||||
ESPB | 6.0 | Status | elective | |
Condition | none | |||
The goal | The course aims to introduce students to data exploration, analysis, and visualisation, as well as the most popular machine learning models and the methodology for their proper use and evaluation. | |||
The outcome | After the completion of the course, the student is expected to: 1) demonstrate understanding of the problem, apply algorithms and ML techniques and define their own problem solving models; 2) acquire a sense of exploration, processing and analysis of data and presentation of the results; 3) learn to develop own application or use existing software tools and libraries. | |||
Contents | ||||
Contents of lectures | Introduction to Data Analysis and ML. Data Preprocessing and Feature Engineering. Exploratory Data Analysis (EDA). Supervised Learning Algorithms. Model Evaluation and Validation. Unsupervised Learning. Time Series Analysis. Introduction to Neural Networks and Deep Learning. Ethics and Interpretability in ML. | |||
Contents of exercises | Visual simulations of theoretical problems. Solving and demonstrations of practical tasks. Analysis of the latest research and scientific papers in this field. | |||
Literature | ||||
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Number of hours per week during the semester/trimester/year | ||||
Lectures | Exercises | OTC | Study and Research | Other classes |
2 | 2 | 1 | ||
Methods of teaching | Lectures with presentations, interactive practical exercises, individual work on projects, laboratory exercises with visual simulations | |||
Knowledge score (maximum points 100) | ||||
Pre obligations | Points | Final exam | Points | |
Activites during lectures | 0 | Test paper | 40 | |
Practical lessons | 30 | Oral examination | 0 | |
Projects | 30 | |||
Colloquia | 0 | |||
Seminars | 0 |