Machine Learning Principles and Concepts

Instructor(s): Prof. K. Diamantaras
Instructor(s): Dr. C. Berberidis
Teaching Hours and Credit Allocation:   30 Hours, 6 Credits
Course Assessment:  Exam & Coursework

 

Aims

Machine learning is an exciting field which entails using computers to run predictive models that learn from existing data and produce conclusions or recommendations. The course covers the fundamental theory, concepts and numerical techniques of machine learning, drawing upon ideas and tools from probability, Artificial Intelligence and algorithm design. The students will also familiarize themselves with common machine learning software libraries and APIs.

 

Learning Outcomes

On completing the course, students 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 data.
  • Understand how to perform evaluation of learning algorithms and model selection

 

Content

  • Introduction to learning from data
  • Classification (KNN algorithm)
  • Decision trees
  • Classifier ensembles
  • Naïve Bayes
  • Clustering (k-means)
  • Model evaluation

 

Reading

  • Tom Mitchell, Machine Learning, McGraw Hill, 1997.
  • Kevin P. Murphy, Machine Learning A Probabilistic Perspective, MIT Press, 2012.
  • Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013.