Introduction to Machine Learning and Data Mining

Course #CS4315

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Clock Est. completion in 3 months

Location pin Offered through Distance Learning

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Overview

A survey of methods by which software and hardware can improve their performance over time. Topics include data manipulation, concept learning, association rules, decision trees, Bayesian models, simple linear models, case-based reasoning, genetic algorithms, and finite-state sequence learning. Students will do projects with software tools. Prerequisites: One college-level course in programming.

Included in Degrees & Certificates

  • 268
  • 367

Prerequisites

  • CY3650

Learning Outcomes

Upon completion of this course the student is expected to: Understand of the basic types of learning methods including: Caching, case-based reasoning, and decision trees Concept learning of logical expressions Classification using probabilistic reasoning Neural networks Heuristic search Evolutionary computing Generative adversarial learning Ensemble learning Be able to recommend the most appropriate learning methods for an application Be able to explain learning methods with paper and pencil. Be able to implement learning methods using a software tool. Identify the major difficulties in implementing and testing learning systems, including explaining reasoning, bias, and adversarial attacks.