CS 419 - Introducing to Machine Learning

Course content
  • 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
  • Selected papers
Pre-requisite : N/A
Total credits : 6 credits - Lecture
Type : Core Course
Duration : Full Semester
Name(s) of other Academic units to whom the course may be relevant : N/A