Machine Learning and Data Mining

Course Description

• Supervised learning (classification, prediction) and unsupervised learning (clustering, associations).
• Statistical models and Bayes rule
• Support vector machines
• Self-Organizing Maps (SOM)
• Principal Component Analysis (PCA)
• Clustering. K-means algorithm
• Reinforcement Learning
• Dimensionality reduction και Sparse Dictionary Learning
• Web or not, Content data mining 

Bibliography

• Machine Learning and Data Mining: Methods and Applications Ryszad S. Michalski (Editor), Ivan Bratko (Editor), Miroslav Kubat (Editor) Publisher: Wiley, ISBN: 978-0-471-97199-3, 1998
• Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition Ian H. Witten; Eibe Frank; Mark A. Hall Publisher: Morgan Kaufmann, ISBN-13: 978-0-12-374856-0, 2011
• Machine Learning in Action Peter Harrington Publisher: Manning Publications, ISBN-10: 1-61729-018-1, 2012
• Building Machine Learning Systems with Python Willi Richert; Luis Pedro Coelho Publisher: Packt Publishing, ISBN-13: 978-1-78216-140-0, 2013