19D111NAM - Advanced machine learning algorithms

Course specification
Course title Advanced machine learning algorithms
Acronym 19D111NAM
Study programme Electrical Engineering and Computing
Module Software Engineering
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      Condition /
      The goal Advanced Machine Learning Algorithms is a course introducing the theoretical foundations of modern machine learning, as well as advanced methods and frameworks used in modern machine learning.
      The outcome The learning outcomes of the course are to enable students to understand statistical and computational considerations in machine learning algorithms, to develop the skill of devising computationally efficient and yet statistically rigorous algorithms for solving machine learning problems and to develop the skill of quantifying the statistical performance of any new machine learning method.
      Contents of lectures Naive Bayes, Logistic Regression, Kernels, Support Vector Machines, Boosting, Linear Regression, Deep Networks, Active Learning, Semi-Supervised Learning, Graphical models, Unsupervised Learning, Dimensional Reduction, Deep Unsupervised Learning, Reinforcement Learning, Non-parametric and High-dimensional Prediction, Prediction and application machine learning in games.
      Contents of exercises /
      1. Tom Mitchell - "Machine Learning" (Original title)
      2. Christopher Bishop - "Pattern Recognition and Machine Learning" (Original title)
      3. Shai Shalev-Shwartz and Shai Ben-David - "Understanding Machine Learning: From Theory to Algorithms" (Original title)
      4. Trevor Hastie, Robert Tibshirani, Jerome Friedman - "The Elements of Statistical Learning: Data Mining, Inference and Prediction" (Original title)
      5. Kevin P. Murphy - "Machine Learning: A Probabilistic Perspective" (Original title)
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      Methods of teaching Presentation, individual work, discussion
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures 0 Test paper 0
      Practical lessons 40 Oral examination 30
      Projects 0
      Colloquia 0
      Seminars 30