CS4315 Introduction to Machine Learning and Data Mining

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.

Prerequisite

CY3650 and one college-level course in programming

Lecture Hours

3

Lab Hours

1

Course 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.