Introduction to Machine Learning and Data Mining

Course #CS4315

Est.imated Completion Time: 3 months

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.

Offerings database access

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Application Deadlines

No upcoming deadlines.