Machine Learning Principles and Concepts

Master's programme(s):
Course code(s):


Course type:




Learning outcomes:

On completing the course, the student will be able to:

  • Develop an appreciation for what is involved in learning from data.
  • Explain a wide variety of learning algorithms.
  • Understand how to apply a variety of learning algorithms to datasets.
  • Know how to perform evaluation of learning algorithms and model selection.
General competences:
  • Search for, analysis and synthesis of data and information, with the use of the necessary technology
  • Decision Making
  • Teamwork
  • Production of free, creative, and inductive thinking

The course introduces fundamental concepts and tools of Machine Learning. The student is exposed to the necessary mathematical/algorithmic background and coding with the Python programming language. The topics covered include:

  • Optimization Techniques.
  • Linear Regression.
  • Linear 2- and multi-class classification.
  • Feature Engineering.
  • Kernel Methods.
  • Fully Connected Neural Networks.
  • Tree-Based Learners.
Full course outline (PDF):