CMPS 470, Spring 2008 Syllabus

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Oct 15, 2013 (3 years and 10 months ago)

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CMPS 470, Spring 2008 Syllabus


Contact Information

Dr. Patrick McDowell


Office: 220 Fayard Hall


Email: patrick.mcdowell@selu.edu


Course Information

In this course the student will be presented with an overview of the Machine Learning.
We will introduc
e 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 s
tudying 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 cour
se 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/a
pproaches 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 Syste
ms; Second
Edition; Michael Negnevitsky












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Course Outline/Schedule (Subject to change)



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 elim
inate

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


3

o

Wha
t 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