Download Objective of the Program

Special Program
“Modern Statistical 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 statistical machine learning and lectures of Professor
John Lafferty
1. Objective
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 modern technologies and sciences have come to rely on our
ability to analyze large-scale and complex data, to draw inference and make
decisions in real-time, resource-constrained situations. Machine 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 larger 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 learning, modern statistics 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.
1
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) Discussion 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 lectures aim to introduce to the development, recent directions and some
challenges in machine learning as well as some basis for statistical machine
learning:
- Machine learning: its roles in other sciences, recent directions and some
challenges
- Model assessment and selection in multiple and multivariate regresion
- Kernel methods and support vector machines
- Dimensionality 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 on Bayesian nonparametric methods and probabilistic
graphical modes
 Instructor: Prof. Nguyen Xuan Long
2
 These lectures introduce to the two above fields of statistical machine
learning:
-
Infinite mixture models based on stick-breaking processes
Dirichlet processes, 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 inference
Topic models
3