Course aims:

This course gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending up with more recent topics such as ensemble methods, support vector machines, and Bayesian networks.
The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work.
Studying algorithms developed based on the paradigms of machine learning.
Analytical study of the advantages and disadvantages of learning-based algorithms automatic versus traditional algorithms in solving problems.
Training to address problems of high complexity from the perspective of learning-based algorithms

Course Entry Requirements:

Artificial intelligence

Course contents:

• Supervised learning. Unsupervised learning • Linear regression • Classification • Decision trees • Ensemble methods • Artificial neural networks • Bayesian learning • Support Vector Machines • Unsupervised learning • Pattern recognition • Feature selection

Teaching methods:

Lecture, conversation, exemplification.

Learning outcomes:

• identify the type of a learning problem; • understand the internal structure of a learning algorithm; • apply a learning algorithm;

Learning outcomes verification and assessment criteria:

Written exam – 50%; continuous assessment – 50%.

Recommended reading:

Tom Mitchell, Machine Learning, The McGraw-Hill Companies, INC, 1997, 200.
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer-Verlag, NY, 2013, 200.
Christopher Bishop, Pattern Recognition and Machine Learning, Springer, NY, 2006, 200.
David Mackay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, NY, 2003, 200.
Ileană I., Rotar C., Muntean M.,, Inteligenţă artificială,, Ed. Risoprint,, NY, 2009, 200.