Download CMPS 470, Spring 2008 Syllabus

CMPS 470, Spring 2008 Syllabus
Contact Information
Dr. Patrick McDowell
Office: 220 Fayard Hall
Email: [email protected]
Course Information
In this course the student will be presented with an overview of the Machine Learning.
We will introduce the topic and study a selection of techniques. The class will be
presented using a both a mix of theory, exercises and programming. Machine Learning is
an interesting topic, and our book covers a broad spectrum of concepts and algorithms.
We will be studying a selection of them and write programs that apply these concepts and
algorithms. Also, each student should get a USB thumb drive in order to save work and
software that may be provided for the class.
Course Objectives
The objectives of this course are for the student to become familiar with the ideas and
concepts of machine learning and to able to apply them to both control/game playing and
classification problems. This course is intended to teach the student to recognize what
type of approach/approaches are needed for a given task and provide a background for
designing and implementing the software to solve that task.
Text
Textbook: Machine Learning; Tom M. Mitchell
Reference books include: Artificial Intelligence A guide to Intelligent Systems; Second
Edition; Michael Negnevitsky
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Course Outline/Schedule (Subject to change)
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Introduction Machine Learning
o Terms
 Knowledge
 Learning
 Understanding
o Tasks
 Control
 Classification
o Approach to problem solving
o Quiz 1
Concept Learning
o If then eliminate
o Candidate Elimination Algorithm
o Homework 1
o Quiz 2
Decision Tree Learning
o Entropy based algorithm
 Concepts
 Setting up code
o Program 1
o Quiz 3
Simulated Annealing
o Relation ship to annealing in metals
o Algorithm
o Program 2
 Dijkstra’s shortest path algorithm
 Traveling Salesman
Genetic Algorithms
o Basics/Terms
 Survival of the fittest
 Natural Selection
 Population
 Chromosomes
 Genes
 Breeding
 Parent Selection
 Crossover
 Mutation
o Solving a problem using a GA
o GA algorithms
 Classic
 Elite
o Quiz 4
o Program 3
Clustering
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o What is clustering?
o Deterministic/Non-Deterministic
o Radial Basis algorithm
o Program 4
o Quiz 5
Neural Networks
o Perceptrons
 Program 5
o Multi-layer networks
 Feed-forward
 Backpropagation
o Self-Organizing Feature Maps
 Program 6
o Quiz 6
Reinforcement Learning
o Cause and effect relationships
o Delayed Reward
 Q learning
 Program 7
 Quiz 7
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