2612 INTRODUCTION TO MACHINE LEARNING AND DATA MINING Course Announcement

[Tricia Hoffman]

2612 INTRODUCTION TO MACHINE LEARNING AND DATA MINING

Patricia Hoffman,PhD is teaching a survey course for the University of California Santa Cruz Extension:  More Info is here:   Machine Learning / Data Mining Survey Course  

http://courses.ucsc-extension.edu/ucsc/search/publicCourseSearchDetails.do?method=load&courseId=1531173

For Credit 3.0 Units  Tue 6:30PM to 9:30PM Sep 13, 2011 to Nov 15, 2011  Number of Sessions: 10

2612 INTRODUCTION TO MACHINE LEARNING AND DATA MINING X470.3 CMPS  Email This Information To Yourself Or A Friend  Remind Me Of This Course At A Later Date back search again Course Description: Machine Learning automatically recognizes complex, previously unknown, novel, and useful patterns and information in all types of data. Data driven algorithms are the wave of the future and their results improve as the amount of data increases. Machine Learning algorithms are used in search engines, image analysis, multimedia database retrieval, bioinformatics, industrial automation, speech recognition, and many other fields. This is a survey course covering the concepts and principles of a large variety of data mining methods. The course will equip you with a working knowledge of these techniques and prepare you to apply them to real problems.  The course covers both supervised and unsupervised learning concepts. The supervised techniques include various types of linear regression, decision trees, k- nearest neighbors, Naive Bayes, Support Vector Machines and ensemble methods. The course also addresses unsupervised techniques such as k-means, expectation maximization, and density based clustering.  The course requires a moderate level of computer programming proficiency, along with an elementary level background in probability, statistics, linear algebra, and calculus. This is a hands-on course using the statistical language R for class examples and homework assignments. No prior knowledge of R is assumed, and some of the basics of open source R language will be covered. Topics Include: Supervised Machine Learning: Various types of linear regression Decision trees K- nearest neighbors Naive Bayes Support Vector Machines Ensemble Methods Unsupervised Machine Learning: K-means Expectation maximization Density based clustering Hierarchical clustering Anomaly detection