TIES411: Konenäkö ja kuva-analyysi
Target: Master-level students.
My status: I am one of three responsible teachers, developing course materials, lecturing and supervising tasks, and responsible for the hands-on demos.
Course level: Advanced
Course actions: This course has a dual approach. Mathematical foundations are introduced and applied into practice using Python. At the same time, students are learning how to work with machine vision sensors, and control devices, to capture and modify the captured images. The demos with Basler sensors and Raspberry Pi cameras deepen the learning experience. The demos will be live.
Exam or demos (5 ECTS)
Learning outcomes: Students understand how to design and build machine vision systems. A student can capture and modify the data and understands the mathematical-physical limitations of machine vision. Students will learn to extract features, work with 3D data, and apply multiple mathematical functions to real-life image-analysis tasks.
Teaching challenges: The course format is new, rolling in period 4 / 2023. The course material is currently under work. This course will be developed within the next years.
Goal – in future, this course could be flipped and materials enlightened with videos
KYBS3050: Koneoppimismenetelmiä kyberturvallisuuteen
Target: Master-level cyber security students.
My status: I am one of two responsible teachers, developing course materials, lecturing and supervising tasks.
Course level: Advanced
Course actions: At first: Python, Jypyter notebook & Sckit learn: Building the environment, testing some classification, clustering and anomaly detection methods for warming up.
We introduce basic machine learning methods and learn how to use them in cyber security issues: Curse of dimensions, SVM, Time series, NLP, MLP, CNN, Autoencoder, CNN autoencoder, Reinforcement learning, transfer learning, GAN, adversial attacks, Minimal Learning Machine.
Demo + essay (5 credits)
Learning outcomes: A student understands machine learning principles and can apply machine learning to various issues related to information security.
Teaching challenges: The students have varying backgrounds; for example, maths is not required. Therefore the course has been traditionally lecture-based, and we provide personal guidance for exercises.
Goal – in future, this course could be flipped and materials enlightened with videos
KYBS7041: Anomalian havaitseminen
Target: Master-level cyber security students.
My status: I am one of two responsible teachers, developing course materials, lecturing and supervising tasks.
Course level: Advanced
Course actions: First: Python, Jypyter notebook & Sckit learn: Building the environment.
Introduction to anomalies and categories. Review of required linear algebra and math skills. Eigenvalue decomposition and singular value decomposition. Data types and data pre-processing and processing. Method evaluation and validation, thresholds, clusters, and neighbour-based methods.
Exam (3 ECTS) or demo + exam (5 ECTS)
Learning outcomes: Students will understand the concept of anomaly and the different categories, concepts of data point, data set and type and different categories. Students will learn the operating principles of different anomaly detection methods and can apply anomaly detection methods in different situations. Can evaluate the performance of methods.
Teaching challenges: The students have varying backgrounds; for example, maths is not required. Therefore the course has been traditionally lecture-based, and we provide personal guidance for exercises.
Goal – in future, this course could be flipped and materials enlightened with videos