Lecture: Machine learning

As (big) data volumes continue to increase, so does the demand for automated analysis of this type of data. As the demand for data scientists grows, so does the need for students who can develop and apply ML technologies in various fields from cybersecurity to medicine and engineering

The course provides an overview of machine learning methods and algorithms for two key learning tasks, supervised and unsupervised learning. In the first part of the course, we will cover the main algorithms and techniques for each task, including experimentation and evaluation aspects. In the second part of the course, we will focus on specific learning challenges, including population imbalance and data sparsity. By the end of the course, you will have learned how to build machine learning models for different problems, how to properly evaluate their performance, and how to overcome specific learning challenges

Course content (subject to change):

• 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