Introduction to Machine Learning and Data Mining

Course #CS4315

Est.imated Completion Time: 3 months


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


  • 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.
Offerings database access
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Application Deadlines

  •  08 Jul 2024

    Fall Quarter applications due

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