TKO_3120 Machine Learning and Pattern Recognition
This course covers the main theories, techniques, and algorithms in machine learning and pattern recognition, starting with simple topics such as linear regression/classification and ending up with more advanced topics such as artificial neural networks and model complexity selection and performance estimation. For pattern recognition most popular feature extraction techniques are introduced and Bayesian decision theory is studied. Both main unsupervised and supervised learning techniques are considered with emphasize on how, why and when they work.
Lectures (28h) given by prof. Jukka Heikkonen, Dr. Csaba Raduly-Baka, Dr. Paavo Nevalainen, Dr. Petra Virjonen
The course has compulsory exercise project.
- Teacher: Jukka Heikkonen
- Teacher: Csaba Raduly-Baka
- Teacher: Petra Virjonen