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26M111APTM - Data Analysis and Machine Learning Techniques

Course specification
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
  1. Data Mining: Practical Machine Learning Tools and Techniques, by Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal; Morgan Kaufmann, 4th Edition, 2016. (Original title)
  2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, by Aurélien Géron, O’Reilly, 3rd Edition, 2023. (Original title)
  3. Machine Learning with Python Cookbook, by C.Albon, O'Reilly Media; 1st edition, 2018. (Original title)
  4. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists, by Alice Zheng and Amanda Casari, O'Reilly Media, 1st Edition, 2018. (Original title)
  5. Linear Algebra and Learning from Data, by Gilbert Strang, 1st edition, Wellesley-Cambridge Press, 2019, ISBN: ‎ 978-0692196380 (Original title)
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