Advanced Machine Learning

Instructor: Prof. Diamantaras
Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework



This course covers advanced topics in machine learning and students are expected to have a solid background in machine learning. The topics to be studied include (among others) Support Vector Machine, graphical models, Kernel methods, unsupervised and semi-supervised learning. Students will gain a deep understanding of both the theoretical and computational aspects of state-of-the-art machine learning and improve their modeling and computational skills by implementing algorithms for some of the above techniques to solve actual problems.


Learning Outcomes

On completing the course, students will be able to:

  • Take an understanding of how to choose a model to describe a particular type of data.
  • Apply the chosen method properly.
  • Know how to evaluate a learned model in practice.
  • Be able to design and implement various machine learning algorithms in a range of real-world applications.



  • Statistical Learning Theory.
  • Mixture Models and the EM Algorithm.
  • Generative and Discriminative Learning.
  • Graphical Models.
  • Hidden Markov Models.
  • Generalization and Model Selection.
  • Support Vector Machines, Kernels.
  • Transduction, Feature Selection.
  • Deep learning.



  • Duda, Hart and Stork, Pattern Classification.
  • Chris Bishop, Neural Networks for Pattern Recognition.
  • Chris Bishop, Pattern Recognition and Machine Learning.
  • Michael Kearns and Umesh Vazirani, An Introduction to Computational Learning Theory, MIT Press, 1994.
  • Mehryar Mohri, Afshin Rostamizadeh and Amit Talwar, Foundations of Machine Learning, MIT Press, 2012.
  • Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.