1
Special Program
“
Modern Statisti
cal Methods in Machine Learning”
18

22/6/2012
:
Lectures on multivariate statistics in machine learning
30/7

3/8/2012
:
Lectures on Bayesian nonparametric methods and probabilistic
graphical modes
6

17/8/2012
.
Discussion on s
tatistical machine learning and lectures of Professor
John Lafferty
1.
O
bjective
Machine learning is an interdisciplinary field which seeks to develop both the
mathematical foundations and practical applications of systems that learn, reason
and act. Machine
learning
is one of the most exciting areas in
computer science
that have witnessed tremendous developments in the past few decades.
Increasingly much of mo
d
ern technologies and sciences have come to rely on our
ability to analyze large

scale and complex da
ta, to draw inference and make
decisions in real

time, resource

constrained situations. M
a
chine learning plays a
central role at the forefront of this broad effort.
Recently,
this field has seen
enormous growth
of
researchers in machine learning gradually
recognized the
field’s broader, unique, and deeper intellectual roots coming from mathematical
statistics and probability theory, optimization, functional analysis, linear algebra,
approximation theory, etc.
Concurrent to the machine learning development,
mathematical statistics including the area of
multivariate data analysis
have also
witnessed substantial changes in the last decades, motivated by the progress of
computational sciences
and real

world engineering and scientific applications
involving large
r and more complex datasets. Statisticians and probabilists have
come to appreciate and embrace the roles of data structures, algorithms and
computational complexity, all of which are fundamental concepts of computer
science. The integration of machine lea
rning, modern st
a
tistics and probability
enables each of these fields to become more powerful.
The objective of the project is two

fold
:
(1)
To introduce to the machine learning community in Vietnam the statistical
machine learning methods developed in recent
years.
(2)
To offer an opportunity for researchers in machine learning in Vietnam to
work together at VIASM in contact with invited scientists in statistical
machine learning.
2
The project is organized by

Prof. Ho Tu Bao
(JAIST
,
http://www.jaist.ac.jp/~bao
)

Prof. Ngo Quang Hung
(SUNY at Buffalo,
http://www.cse.buffalo.edu/~hungngo
)

Prof. Nguyen Xuan Long
(Michigan Univ.,
http://www.stat.lsa.umich.edu/~xuanlong
)
Especially, the project has the participation of Professor John Lafferty from
Chicago
University
, a leading expert in statistical machine learning
(
http://newfaculty.uchicago.edu/psd/lafferty.shtml
).
2.
Program
The project activities consist of
:
a)
Lectues on a number of selected topics in statistical machine learning
,
b)
Research on the problems proposed by the project researchers,
c)
Discu
ssion on statistical learning topics with experts.
The project is carried out in the period from 18th June to 17 August, 2012 with the
following plan:
18

22
/6/2012
:
Lectures on multivariate statistics in machine learning
Intructor
:
Prof. Ho Tu Bao
The l
ectures aim to introduce to the development, recent directions and some
challenges in machine learning as well as some basis f
or statistical machine
learning
:

Machine learning: its roles in other sciences,
recent directions
and
some
challenges

Model assess
ment and selection
in mult
iple and multivariate regresion

Kernel methods and support vector machines

D
imensionality
reduction và
manifold learning

Topic models in text analysis.
25/6

27/7/2012
.
Project researchers work at VIASM
30/7

3/8/2012
:
Lectures o
n Bayesian nonparametric methods an
d probabilistic
graphical modes
Instructor
:
Prof. Nguyen Xuan
Long
3
These lectures introduce to the two above fields of statistical machine
learning
:

Infinite mixture models based on stick

breaking processes

Dirichlet p
rocesses, stick

breaking processes, Chinese restaurant processes

Markov Chain Monte Carlo
algorithms for infinite mixtur

Hierarchical nonparametric Bayes

Nonparametric Bayes for learning latent network structures

Asymptotic theory for statistical inference
in infinite mixtures

Variational inference and message

passing algorithms
.
6

17/8/2012
.
Discussion on statistical machine learning and lectures of Professor
John Lafferty on
:

Sparsity in regression

Graphical
model structure
learning

Nonparametric inferen
ce

Topic models
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