Lecture - Machine Learning

This course provides an introduction to the fundamental concepts, methods, and algorithms of Machine Learning (ML). The first part of the course focuses on supervised and unsupervised learning, covering core machine learning algorithms, experimentation, and evaluation techniques. The second part of the course addresses important learning challenges commonly encountered in real-world applications, including class imbalance and data scarcity.  By the end of the course, students will have learned how to develop and analyze machine learning models for different learning tasks, properly evaluate their performance, and develop robust solutions under challenging data conditions.

Learning objectives. By the end of the course, students will be able to:

• Understand the core principles of ML
• Apply supervised and unsupervised learning methods
• Analyze and evaluate ML models
• Understand challenges related to imbalanced and limited data
• Select appropriate methods for different learning tasks
• Interpret experimental results and model performance

Course content:

(Topics may be adjusted slightly during the semester)

• Supervised learning
• Unsupervised learning
• Outlier detection
• Machine learning with imbalanced data
• Machine learning under data scarcity

Literature:

• Shai Ben-David and Shai Shalev-Shwartz, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014
• Mitchell T. M., Machine Learning, McGraw-Hill, 1997
• Wagner Meira and Mohammed Zaki, Data Mining and Machine Learning: Fundamental Concepts and Algorithms, Cambridge University Press, 2020