CS 419 - Introducing to Machine Learning
This course will provide a broad overview of Machine Learning with a stress on applications.
Supervised learning: Decision trees, Nearest neighbor classifiers, Generative classifiers like naive Bayes, Support vector Machines
Unsupervised learning: K-Means clustering, Hierarchical clustering, EM, Itemset mining
Applications: image recognition, speech recognition, text and web data retrieval, bio-informatics, commercial data mining.
Tom Mitchell, Machine Learning. McGraw-Hill, 1997. 302240302240302240
Pattern recognition and machine learning by Christopher Bishop, SPringer Verlag 2006 302240302240302240
6 credits - Lecture
Name(s) of other Academic units to whom the course may be relevant