UNIVERSIDAD TECNICA FEDERICO SANTA MARIA

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Nov 7, 2013 (3 years and 7 months ago)

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UNIVERSIDAD

TECNICA


FEDERICO

SANTA

MARIA





COURSE

TITLE
:


SEMINAR

ON

SOFT

COMPUTING

CODE
:

IPD
-
434

PREREQUISITES
:

ELO320

-

ALGORITHMS

AND

DATA

STRUCTURES

CREDIT
S:

4


LEC.

HRS/WK:

4


TA

HRS/WK:


L
AB.

HRS/WK:


EXAM:

NO



OBJECTIVES
:

Students

will

study

soft

computing

methods

and

areas

where

these

technologies

are

used.

Methods

to

be

studied

include

decision

trees,

neural

networks,

evolutionary

computation,

Bayesian

networks,

diffuse

logic
,

boosting,

hybrid

methods,

and

other

machine

learning

methods

such

as

Support

Vector

Machines

(SVM).


Some

of

the

applications

to

be

studied

include

signal

processing

and

analysis

(digital

fingerprints,

voice,

video,

writing
,

bio
-
informatic

sequences
),

co
ntrol

of

industrial

processes

(e.g.

power

plants

and

stations),

robotics,

and

real
-
time

systems,

among

others.



METHODOLOGY
:

For

each

topic,

theoretic

foundations

are

discussed,

and

a

literary

review

of

relevant

work

is

conducted.

Then

current

research

i
s

reviewed

and

publications

with

innovative

ideas

are

studied.

Students

will

develop

a

specific

soft

computing

application

as

part

of

a

course

project.

Students

must

study

the

state

of

the

art

in

the

area

and

must

do

a

research

paper

on

a

specific

topic

re
lated

to

the

course

content.

The

paper

must

be

in

a

format

acceptable

for

presentation

in

an

international

conference,

and

it

will

be

presented

to

the

class

at

the

end

of

the

semester.



CONTENT
:

1.

Introduction
.

2.

Feature

selection

and

classification

methods.


3.

Modeling

and

optimization

methods.

4.

Evolutionary

algorithms.

5.

Neural

networks.

6.

Diffuse

logic.

7.

Hybrid

methods.


REFERENCES:

1.

F

Karray,

C

De

Silva,

"Soft

Computing

and

Intelligent

Systems

Design",

Addison

Wesley
,

2004

2.

Ethem

Alpaydin,


Introduction

to

Machine

Learni
ng”
,

MIT

Press
,

2004

3.

T

Mitchell,

"Machine

Learning",

McGraw
-
Hill
,

1997.

4.

Jyh
-
Shing

Roger

Jang,

Chuen
-
Tsai

Sun,

Eiji

Mizutani,

"Neuro
-
Fuzzy

and

Soft

Computing:

A

Computational

Approach

to

Learning

and

Machine

Intelligence",

Pearson

Education
,

1997


P
repared

by

:

Tomás

Arredondo

V.

09
-
05
-
2007

Notes
:


Approved

by

:



Date

: