Course ID: AC6310

Title: Machine learning


Person in charge:

            Dr. Hai Vu

Teaching semester:


Target skills:

Today, in all domains of science and technology it is necessary to have computer systems able to work autonomously and to build knowledge from raw data coming from the Web or from huge data-bases. Machine Learning concerns the study and implementation of systems able to perform these tasks. The intent of this course is to propose a broad introduction to field of Machine Learning, including discussions of each of the major approaches, symbolic and numeric, currently being investigated and achievements they can be done. Another goal is to compare the advantages and drawbacks of these various methods and to explain under which conditions each is most appropriate

Program summary:

  • Introduction to Artificial Intelligence and Machine Learning domains
  • Principles of learning: generalization, version space, etc.
  • Learning of decision rules: Decision tree, ILP, etc.
  • Clustering of structured data
  • Proximity measures for temporal data
  • Supervised /non-supervised classification of temporal data
  • Introduction to neural networks
  • Support Vector Machine (SVM)
  • Introduction to Bayesian networks, Learning in bayesian networks

Used software: