Machine learning fundamentals

  • Course teacher Marko Đurasević
  • Associate teachers -
  • ECTS credits 5
  • Number of hours: Lectures + Seminars + Exercises 45 0 15

Course objectives

The goal of the course is to give students the basic knowledge of machine learning fundamentals. This includes knowledge about different classification, regression, and clustering methods, dimensionality reduction, and model selection. The students will also gain knowledge about using an open source machine learning library and applying it for performing various machine learning tasks.

Enrolment requirements and/or entry competences required for the course

-

Learning outcomes at the level of the programme to which the course contributes

  • Apply theoretical knowledge of the fundamentals of the six core disciplines and their relationship within cognitive science.
  • Critically evaluate cognitive science findings and synthesize information to be employed in a collaborative professional environment.
  • Participate in data-driven innovation projects and apply appropriate data science tools.
  • Apply AI tools in concrete tasks and practical contexts.

Course content (syllabus)

  1. Introduction to machine learning. Basic concepts and definitions.
  2. Regression. Regularization.
  3. Linear discriminative models. Logistic regression. Perceptron.
  4. Support vector machines. Kernel machines.
  5. Non-parametric methods. K-nn. Decision trees.
  6. Parameter estimation. Gaussian processes.
  7. Bayesian estimation. Naïve Bayes.
  8. Midterm exam.
  9. Combining classifiers. Ensembles. Random forest.
  10. Clustering methods. K-means. K-medoids method.
  11. Clustering methods. Hierarchical clustering. DBSCAN. OPTICS.
  12. Model validation and selection. Cross validation. Performance evaluation.
  13. Feature selection and reduction.
  14. Reinforcement learning.
  15. Final exam

Student responsibilities

Class attendance, completing assignments on time.

Required literature

  • Ethem Alpaydin, Introduction to Machine Learning
  • Peter Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Optional literature

  • Christopher Bishop, Pattern Recognition and Machine Learning
  • Andreas C. Müller, Sarah Guido, Introduction to Machine Learning with Python A Guide for Data Scientists