Machine Learning & Data Mining

Syllabus

Week 1: Machine Learning (Types, Tasks, and Applications)
Week 2: Classification/Regression Trees.
Week 3: Evaluating a Model.
Week 4: Instance Based Learning (k-NN, weighted distance k-NN).
Week 5: Designing an ML System, Data Mining Systems.
Week 6: Regression, Ensemble Methods (Bagging, Boosting, Stacking).
Week 7: Clustering (K-means, hierarchical clustering, density-based).
Week 8: Bayesian Learning.
Week 9: Association Rules.
Week 10: Neural Networks: Perceptrons, Multi-Layer Perceptrons, Back Propagation.
Week 11: Issues in Neural Network Training.
Week 12: Deep Learning / Deep Neural Networks.
Week 13: The KDD process – Issues in Knowledge Discovery.

Suggested Bibliography

• Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar. “Introduction to Data Mining – 2nd Edition“, ISBN  :0133128903, Pearson, 2018.
• Κ. Διαμαντάρας και Δ. Μπότσης, “Μηχανική Μάθηση“, ISBN: 978-960-461-995-5, Εκδόσεις ΚΛΕΙΔΑΡΙΘΜΟΣ ΕΠΕ, 2019 (Ελληνικά / ΕΥΔΟΞΟΣ: 86198212)
• Ethem Alpaydin, “Introduction to Machine Learning – Fourth Edition”, The MIT Press, 2020
• Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. 2nd Edition, Bing Liu, Springer, 2011
• Ι. Βλαχάβας, Π. Κεφαλάς, Ν. Βασιλειάδης, Φ. Κόκκορας και Η. Σακελλαρίου, “Τεχνητή Νοημοσύνη – 4η Έκδοση“, Εκδόσεις Πανεπιστημίου Μακεδονίας, ISBN: 978-618-5196-44-8, 2020 (Ελληνικά / ΕΥΔΟΞΟΣ: 94700120)