CS 443 Machine Learning

Catalog Description: 
Introduction to the field of modeling learning with computers. Topics included are explorations of inductive learning, learning decision trees, ensemble learning, computational learning theory and statistical learning methods.
Prerequisite: 
Junior standing and CS 428 or permission of the instructor
Credits: 
3
Offered: 
Second semester—even years
Required or Elective: 
Elective for the BS in Computer Science.
Level: 
Advanced
Coordinator: 
John Boon
Current Textbook: 

Introduction to Machine Learning, Third edition. E Alpaydin, The MIT Press 2014 (ISBN 9780262028189).

Topics covered: 
  • Supervised Learning
  • Bayesian Decision Theory
  • Parametric Methods
  • Multivariate Methods
  • Dimensionality Reduction
  • Clustering
  • Nonparametric Methods
  • Decision Trees
  • Linear Discrimination & Multilayer Perceptrons
  • Local Models
  • Kernel Machines
  • Graphical Models
  • Hidden Markov Models
  • Reinforcement Learning
  • Design and Analysis of Machine Learning Experiments
Student Learning Outcomes: 

On completing this course, the student will be able to:

  • Explain the differences among the three main styles of learning: supervised, reinforcement, and unsupervised.
  • Implement simple algorithms for supervised learning, reinforcement learning, and unsupervised learning.
  • Determine which of the three learning styles is appropriate to a particular problem domain.
  • Compare and contrast each of the following techniques, providing examples of when each strategy is superior: decision trees, neural networks, and belief networks.
  • Implement a simple learning system using decision trees, neural networks and/or belief networks, as appropriate.
  • Characterize the state of the art in learning theory, including its achievements and its shortcomings.
  • Explain the nearest neighbor algorithm and its place within learning theory.
  • Explain the problem of overfitting, along with techniques for detecting and managing the problem.
Relation of Course Outcomes to Program Outcomes: 
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Role in Assessment: 
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