Short Course on Topological Data Analysis By IIT Bombay
The growth of Big Data has expanded the traditional data science approaches to address the
multiple challenges associated with this field. Moreover, the wealth of data available nowadays
from a wide range of sources has fundamentally changed the needs for theoretical methods, and
thereafter challenged theoreticians to provide insight into this area. One of the recently emerged
and increasingly popular trends in Data Analysis is to apply tools based on the algebraic topology.
The underlying idea is to free the data scientists from the necessity to pre-define a model, but
rather to derive the underlying space parameterizing the data from the data itself. The literature
in this new area known as Topological Data Analysis is growing quickly, interacting with nascent,
emerging, and classical topics such as machine learning, biological networks, pattern recognition,
control theory, communication, and signal processing among many others others.
The goal of the minicourse is to give engineering students with little or no background in
algebraic topology a quick introduction to the tools of the discipline, and to several modern
applications with special emphasis on control theory. About 40% of the time will be spent on
developing background in algebraic topology, and about 60% of the time will be devoted to
applications. The prerequisites would assume basics of multivariable calculus and linear algebra.
- Yuliy Baryshnikov is a Professor of Mathematics and Electrical and Computer Engineering,
University of Illinois at Urbana-Champaign, Illinois, USA. Yuliy’s research straddles the vast domains of geometry, analysis, and probability. His research interests in engineering center around control theory, networks, operations research, mathematical economics, applications in physics and biology, and his interests in mathematics center around applied topology, dynamical systems and singularities, probability theory and stochastic processes. His expertise in a bewildering variety of mathematical disciplines is the central element behind his unusual solutions to crucial problems that have remained open for long.
- Debasish Chatterjee is an Associate Professor with Systems & Control Engineering, IIT Bombay, India. His research interests lie in constrained control, the interface of machine learning and control theory, and stochastic and optimal control.
12-24 February, 2018
Indian Institute of Technology Bombay
- Participants from India
Academic institutions: 10,000 per person
Students: 3,000 per person
Industry: 20,000 per person
Participants from beyond Indian borders: US$500 per person
The above fees include all instructional material, computer use for tutorials and assignments, 24 hr free internet facility. The participants will be provided with accommodation on payment basis.
Phone number: +91 22 2576 7879
For further information visit here.