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 semesterâ€”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:

See the pages "BSCS Course Matrix" and "BSCS Courses for Assessment"

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Current syllabus: