Students become familiar with the main concepts as well as with specific data analysis and machine learning techniques and become also familiar with many applications of it. They also acquire skills in reviewing scientific papers, giving scientific presentations and working in practice applying various machine learning algorithms on data of different type.
Course Content (Syllabus)
Introduction, Supervised Learning, Decision trees, Rule learning, Case based learning, Bayesian learning, Neural Networks, Support Vector Machines, Model Evaluation, Clustering, Association Rules, Feature Selection and Discretization, Ensemble Methods, Reinforcement Learning, Applications in Python.
Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Classification, Regression
Additional bibliography for study
- Machine Learning, T. Mitchell, McGraw Hill, 1997.
- The Elements of Statistical Learning:Data Mining, Inference, and Prediction, T. Hastie, R. Tibshirani and J. Friedman, Springer, 2nd edition, 2009.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, 2019
- Introduction to Machine Learning. Ethem Alpaydin, The MIT Press, March 2020.
- Introduction to Machine Learning with Python: A Guide for Data Scientists, Sarah Guido and Andreas C. Müller, O' Reilly Media, 2016.
- Τεχνητή Νοημοσύνη, Δ' Έκδοση, Ι.Βλαχάβας, Π.Κεφαλάς, Ν. Βασιλειάδης, Φ.Κόκκορας και Η. Σακελλαρίου. Εκδόσεις Πανεπιστημίου Μακεδονίας, Θεσσαλονίκη 2020
- Introduction to Machine Learning (Draft of incomplete Notes), Nils J. Nilsson, 2015 (https://ai.stanford.edu/~nilsson/mlbook.html)