Foundations of Machine Learning for Cybersecurity

This 6 ECTS course consists of 16 x 2-hour lectures. It begins with a comprehensive introduction to core machine learning concepts, including supervised and unsupervised learning, deep learning fundamentals, and performance evaluation techniques. The course then explores how these methods can be applied to cyber security problems such as intrusion detection, malware classification, anomaly detection in network traffic, phishing detection, and fraud prevention. Students will participate in a semester-long individual project, applying machine learning techniques to a real-world cyber security challenge. The course has no prerequisites, but prior exposure to programming or data science concepts would be beneficial.

NB! This course will take place in autumn semester 2026/2027 which starts on 31st of August and ends on 24th of January (you can find that information under start date section). TalTech's timetables for autumn semester 2026 will be published at the end of June via tunniplaan.taltech.ee.

After completing this course the student:

• explains the fundamental concepts of the Machine Learning (ML) and Deep Learning (DL) workflows;
• identifies the strengths and weaknesses of applying ML and DL methods to address cyber security challenges;
• plans and applies ML and DL methods to analyze and mitigate cyber threats;
• employs research methodologies to design and evaluate ML solutions for security problems;
• designs a research project applying ML and DL methods to address a specific cybersecurity problem and justifies the selected methodology;
• presents the methodology and results of the proposed research project appropriately, both in written form as a scientific article and as an oral presentation.

Final assessment can consist of one test/assignment or several smaller assignments completed during the whole course. After declaring a course the student can re-sit the exam/assessment once. Assessment can be graded or non-graded. For specific information about the assessment process please get in touch with the contact person of this course. For specific information about grade transfer please contact your home university

Monday's 17:45 - 19:45

• - Geron, Aurélien. Hands-On Machine Learning with Scikit-Learn and Pytorch: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media, 2025
• - Chollet, François, & Watson, Matthew. Deep Learning with Python, Third Edition. Manning Publications, 2025
• - Raschka, Sebastian, Liu, Yuxi, & Mirjalili, Vahid. Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python. Packt Publishing, 2022

lectures