Cognitive: Upon successful completion of the course, students will know what is involved in the field of engineering learning, as well as how algorithms for linear models, tree models, rule models, ensembles of models and reinforcement learning work.
Skills: In addition, they will be able to apply such algorithms to real-world data and applications using Python's scikit-learn and gym libraries.
Course Content (Syllabus)
Introduction to Machine Learning. Linear Models. Tree Models. Rule Models. Model Ensembles. Reinforcement Learning.
machine learning, linear models, decision trees, rule learning, ensemble methods, reinforcement learning
Course Bibliography (Eudoxus)
- ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ,86198212, ΚΩΝΣΤΑΝΤΙΝΟΣ ΔΙΑΜΑΝΤΑΡΑΣ, ΔΗΜΗΤΡΗΣ ΜΠΟΤΣΗΣ, ISBN: 978-960-461-995-5, ΕΚΔΟΣΕΙΣ ΚΛΕΙΔΑΡΙΘΜΟΣ ΕΠΕ
- ΑΝΑΓΝΩΡΙΣΗ ΠΡΟΤΥΠΩΝ ΚΑΙ ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ, 86053413, C.M. Bishop, 9789603307907, ΓΡΗΓΟΡΙΟΣ ΧΡΥΣΟΣΤΟΜΟΥ ΦΟΥΝΤΑΣ
Additional bibliography for study
- Σημειώσεις μαθήματος
- Machine Learning, Tom Mitchell, McGraw-Hill Education, 1997.
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, O'Reilly Media, 2017.