Introduction to Machine Learning

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15 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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Com
p
l
e
ted r
e
gi
s
t
r
a
t
i
on f
o
rms

s
hould be
se
nt

to t
h
e

c
our
s
e

c
o
ordin
a
t
o
r
a
t
t
he
f
o
l
l
o
w
ing

a
ddre
ss
:




VENU
E FOR C
L
A
SSE
S


C
o
u
r
se
w
i
l
l be

h
el
d at

Lab block, SPSU.


G. Iyyakutti Iyappan

Cou
rse
Coo
r
di
n
a
t
o
r

Electronics and Communication
Engineering

Sir Padampat Singhania University

Bhatewar

Udaipur
,


Rajasthan


313 601
.


P
hone
:

+91 2957
-
226095 (Ext


350)

F
a
x
:

+91 2957
-
226094

e
-
m
ail
:
iyyakutti.iyappan@spsu.ac.in




IM
P
O
R
T
A
N
T

D
A
T
E
S



L
a
s
t

d
a
t
e

f
or

r
e
ce
i
pt

o
f
r
e
g
i
s
t
r
ati
o
n
:

April 30
,

2
0
13


N
ot
i
f
i
cati
o
n
o
f

acc
e
p
t
a
n
c
e:

May

3
, 2
0
13


C
o
u
r
se d
a
t
es:

M
ay

13



16
, 2
0
13


N
ote:



Inc
o
m
p
lete

ap
p
li
c
ati
o
n

f
o
r
ms

w
ill

n
ot

be
en
t
e
r
t
ained.



For

ad
d
itio
n
al

co
p
i
es

of

t
he

r
eg
i
strati
o
n

fo
r
m,
ple
a
se

use

a

pho
t
oco
p
y

or

t
y
pe

in

t
he

f
o
r
m
at
gi
v
e
n
.

RE
GI
S
T
R
A
T
IO
N


C
a
n
d
i
d
a
t
es

sh
o
u
l
d

comp
l
ete

t
h
e

e
n
c
l
os
e
d
r
e
g
i
s
tr
a
t
i
on
f
o
r
m
,

a
n
d

s
e
nd

i
t

by
m
a
i
l

or

f
ax
t
o
t
he
C
o
o
r
d
i
n
a
t
or.

C
o
n
f
i
rm
a
ti
on
of

eligible

candidates

wil
l
be

on

a

f
i
r
st

c
o
m
e

f
i
r
st

ser
v
ed

b
a
s
i
s

up

t
o

a
m
a
x
i
m
um

o
f

30

ca
n
d
i
d
at
es.

The coordinator

should receive the completed

reg
i
strat
i
on forms by

April
30
, 2013
.


Course Fee
: Rs.500 per participant


E
LI
G
I
B
I
LI
T
Y


It is an inter disciplinary course applicable to all
departments.
F
a
cu
l
t
y
m
emb
e
r
s
of
e
n
g
i
n
e
eri
n
g
co
ll
e
g
es

and universities
, Students

and Industry
persons

are

e
li
g
i
b
l
e

t
o

at
t
e
n
d

t
h
i
s co
u
r
se
.




F
o
r

a
n
y

f
ur
t
h
e
r

i
n
f
o
r
m
at
i
on

r
e
g
a
r
d
i
ng

pr
o
gr
a
m
s at
SPSU
,

p
l
e
a
se

contact
:


Registrar

Sir Padampat Singhania University

Bhatewar

Udaipur 313 601, Rajast
han

Telephone: +91 2957
-
226095
(6 Lines)

Telefax: +91 2957
-
226094

Email:
info@spsu.ac.in







Sho
r
t

T
erm

C
o
ur
s
e on

Introduction to Machine Learning


M
a
y

13

-

1
6
,

2
0
1
3












Co
o
rdin
a
t
o
r

G
.

Iyyakutti Iyappan


ECE Department

Sir Padampat Singhania University
,

Udaipur
,

Rajasthan

313 601
,

INDIA.


INT
R
ODU
C
TION


The field of machine learning is concerned with the
question of how to construct computer programs
that improve
automatically with experience
.
This
introductory course on machine learning will give an
overview of many concepts, techniques, and
algorithms in machine learning, beginning with
topics such as linear regression and ending up with
more recent topics such a
s boosting, support vector
machines, hidden Markov models, and Bayesian
networks. The course will give the audience the
basic ideas and intuition behind modern machine
learning methods as well as a bit more formal
understanding of how, why, and when they
work
.


About the course


Machine learning (ML) is one of the fastest growing
areas of science. It is largely responsible for the rise
of giant data companies such as Google, and it has
been central to the development of lucrative
products, such as
Microsoft’s Kinect, Amazon’s
recommender system, the spam detection systems
of Facebook, and the advertising engines of these
and many other companies. ML is the key enabling
technology behind face detection in consumer
cameras, news personalization, book
and movie
recommender systems, image and video search,
credit card fraud detection, speech recognition
systems, and many more applications that most
people have begun to take for granted. ML has also
begun to make it possible to have automatically
-
driven c
ars, more efficient energy management
systems, and improved systems for health
-
care
management.








C
O
URS
E
C
O
N
T
E
N
T


Introduction


F
oundational issues,

linear algebra
(

vector


spaces and decision surfaces
)

and simple


learning methods including
perceptron



learning



Neural Networks and Boosting

Applications


Linear prediction

Regularization and linear regression

Bayesian learning


Nearest Neighbour Based Classifiers

Combination of classifier

Clustering

Hidden Markov Models

S
upport vector
machine

and kernel
-

based learning


Decision trees


D
imensionality reduction methods


Genetic algorithms


Applications (
recognition,
detection

and text

classifications
)


BOARDING & LODGING
:

Boarding and lodging provided in the Student
Hostels

will be chargeable

(Rs 150/day)
.


A request
is to be made in this regard at the time of
registration.



S
ho
rt

T
e
r
m

C
o
u
rse

o
n

Introduction to Machine Learning


M
ay

13

-

1
6
, 2
0
13


R
e
gis
t
rat
i
on

Fo
r
m


N
a
me
(
in
b
l
o
ck lette
r
s
)
:

(
M
r
.
/
M
rs
.
/
M
s
.
)



Desi
gna
ti
on
:




Org
an
i
z
a
ti
on:




M
a
iling

A
dd
r
es
s
:





Tele
phon
e
:




F
a
x:





Em
a
il:
_



E
du
c
a
ti
o
n
a
l

Q
ua
lif
i
c
a
ti
o
n
:




Ex
p
erie
n
c
e:




Ac
co
mm
oda
ti
o
n

in

C
a
m
pu
s
*
:
Y
ES /

NO

S
i
gna
t
u
r
e
o
f

A
pp
lic
a
n
t:




Spon
s
o
rs
h
ip

a
n
d

s
i
gna
t
u
r
e
o
f

P
r
i
n
ci
pa
l

o
f

t
h
e
C
o
lle
g
e /

In
s
titute

(
w
i
th
da
te

&

s
e
a
l)



*

I

ag
r
ee to

pa
y the

co
s
t

o
f
R
oom
,
a
s
p
er

the

r
ul
e
s
.