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

Artificial intelligence

• 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

Lecture, conversation, exemplification.

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

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

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