COM 307 MACHINE LEARN/DATA MINING
An introduction to the basic theory, concepts, and techniques of machine learning and data mining, including decision trees, neural networks, logistic regression, and data preparation, modeling, and presentation. Data mining techniques, including clustering, classification, associations, deviation detection, and link analysis will be covered. Methods, such as hidden Markov models and support vector machines, will be applied to a variety of applications, including electrical signal analysis (mostly of biomedical origin, such as EEG, ECG, and channel current analysis), genomics, and finance. Data mining tools will be introduced and used to complete a project on real-world data.
Enrollment Limit
Enrollment limited to 28 students.