CIS 490: Machine Learning

Prereq: CIS 360; C or Better

General Education requirement: Natural Science Technology

Constructing computer programs that automatically improve with experience is the main task of machine learning. The key algorithms in the area are presented. Learning concepts as decision trees, artificial neural networks and Bayesian approach are discussed. The standard iterative dichotomizer (ID3) is presented, the issues of overfitting, attribute selection and handling missing data are discussed. Neural nets are discussed in detail, examples of supervised and unsupervised learning are presented. Instance-based learning, i.e. k-nearest neighbor learning, case-based reasoning are introduced. Genetic algorithms are discussed on introductory level.

Course information

3.00 credits
Section 01: Undergraduate Lecture
Instructor(s): 

Class: #11748

Tue, Jan 21, 2020 - Wed, Apr 29, 2020

Status: 

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