Machine learning 2 | Faculty of Technical Sciences

Subject: Machine learning 2 (17.EK471)

General information:

Category	Theoretical-methodological
Scientific or art field	Telecommunications and Signal Processing
ECTS	6

This course focuses on advanced machine learning topics with an emphasis on the theoretical foundations and the advanced implementation tools/practices. The topics go deep into specific supervised, unsupervised and semi-supervised algorithms addressing state-of-the-art machine learning techniques.

Students will be able to interpret and interrelate different advanced ML algorithms and approaches. Students will know how to approach the data, identify and select the most convenient ML approaches, regularization techniques, monitor training and adjust regularization parameters. Students will master the use of Python based toolboxes.

Neural Networks: Introduction, architectures and training procedures, evaluation and application. Ensemble learning: Bagging and boosting. Clustering - advanced algorithms, mixture models and expectation-maximization (EM) algorithm, ensemble clustering. Semi-supervised algorithms. Hidden Markov models. Probabilistic graphical models (inference, belief propagation, practical application).

Lectures, computer lab sessions (Matlab, Python), homework, consultations, active learning, project and research based learning, workshops.

Authors	Title	Year	Publisher	Language
Goodfellow, I., Bengio, Y., Courville, A.	Deep Learning	2017	MIT Press, Cambridge	English
Khanna, T.	Foundations of Neural Networks	1990	Addison-Wesley, Massachusetts	English
Hastie, T., Tibshirani, R., Friedman, J.	The Elements of Statistical Learning : Data Mining, Inference, and Prediction	2009	Springer, New York	English
Bishop, C.M.	Pattern Recognition and Machine Learning	2006	Springer, New York	English
Kevin Murphy	Machine Learning: A Probabilistic Perspective	2012	MIT Press	English