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.
Junior standing and CS 428 or permission of the instructor
Second semester—even years
Required or Elective: 
Elective for the BS in Computer Science.
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: 
Role in Assessment: 
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