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<h3>Approved Courses</h3>
<br><br>
<h4>1. Fairness, Accuracy and Transparency in Machine Learning</h4>
<br>
<b>Overview</b>
<p align='justify'>
Automated decision making is a key component of the so called smart solutions, and like all other countries, India too is in the process of putting in place smart solutions in diverse areas– be it monitoring sewage treatment plants, or in scheduling public transport, in disaster management, or in controlling the spread of a deadly disease, etc. Automated decision making now is essentially data driven– where the data is huge– and what is needed is to find clusters, make use of classifiers, or, more generally, find patterns in a large dimensional huge data space. We need to use machine learning to carry out these tasks. As huge data drive decision making in all aspects of our life, whether it is for marketing, information gathering and search, or even for medical diagnosis, prison sentencing or for financial decision making, machine learning either already is, or is going to be everywhere.
</p>
<p align='justify'>
Automated decision making through machine learning is very attractive: it is much faster than human decision making, is able to find patterns in much larger collections of data, and can integrate different sources in a way that humans find difficult, if not impossible. But is it any better? It has often been argued that decisions based on machine learning are less biased than human decision making because mathematics is blind. But a number of high profile embarrassments for automated decision making have shown that while algorithms may not always be biased in the same way that humans are, they are often trained to reflect biases and prejudices in the underlying data they are trained on. What is worse is that as machine learning algorithms become more and more complicated, it is even harder to understand why they make their decisions the way they do. In other words, it is becoming increasingly clear that machine learning algorithms may not be fair, they are not accountable, and they are far from transparent.
</p>
<p align='justify'>
The objective of this course is to expose students in data analysis to the many challenges in making machine learning tools fair, accountable and transparent, and to discuss the currently available solutions. The course is for 2 credits with 15 one-hour lectures and 5 one-hour tutorial/discussion sessions.
</p>
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<td bgcolor='#0B4C5F'><h4 style="color:white">Modules</h4>
</td><td width='70%'>
<ul>
<li>> How are machine learning algorithms evaluated? How do we interpret their results? (1 Lecture)</li>
<li>> Fairness: (6 lectures)
<dir>
– What is fairness?<br>
– Fairness-preserving methods: modifying the classier, or modifying the data, or modifying the results.
</dir>
</li>
<li>> Interpretable machine learning: (4 Lectures)
<dir>
– How can we interpret the results of machine learning in a user-friendly way,<br>
– Can we generate causal explanations as opposed to correlations?
</dir>
</li>
<li>> Verifiable learning (2 Lectures)
<dir>
– Can we verify the results of a machine learning task even if we can’t compute the results ourselves?
</dir>
</li>
<li>> Case studies of bias in machine learning (2 Lectures)</li>
<li>> Tutorials/Discussions (5 Hours)</li>
</ul>
</td></tr>
<tr valign='top'>
<td bgcolor='#0B4C5F'><h4 style="color:white">Who can attend...</h4></td>
<td>
<ul>
<li>> Faculties, Engineers, Scientists, and Researchers from academic, industrial and government organizations including R&D laboratories.
</li>
<li>> Students at all levels (BE/BTech/MSc/ME/MTech/PhD/Other) from academic and technical institutions/universities from India or abroad.
</li>
</ul>
Number of participants for the course will be limited to fifty. Preference will be given to the participants opting against credits.
</td></tr>
<tr valign='top'>
<td bgcolor='#0B4C5F'><h4 style="color:white">Fees</h4></td>
<td>
The participation fees for taking the course is as follows:
<dir>
Participants from abroad : US $500<br>
Industry/Research Organizations: INR 10,000<br>
Academic Institutions: INR 2,000 (half for SC/ST students)<br>
</dir>
<p align='justify'>
The above fee include all instructional materials, computer use for tutorials and assignments, laboratory equipment usage charges, 24 hr free internet facility. The participants will be provided with accommodation on payment basis.
</p>
</td></tr>
</table>
<br>
<h4>The Faculty</h4>
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<dir>
<p align='justify'>
<img src='images/sb.jpg' width='50' height='50' />
<b><a href='http://www.iiita.ac.in/administration/message_from_director/'>Prof. Somenath Biswas</a></b> is the Director of Indian Institute of Information Technology Allahabad. He has been the President of the Indian Association for Research in Computing Science (2000-2002). His research interests are in the field of computational complexity theory, randomized algorithms, computational biology, logic in computer science.
</p>
<p align='justify'>
<img src='images/sv.jpg' width='50' height='50' />
<b><a href='http://www.cs.utah.edu/~suresh/web/'>Dr. Suresh Venkatasubramanian</a></b> is an Associate Professor at the School of Computing at the University of Utah. His research interests are in the field of algorithms and computational geometry, with a current focus on data mining and large-data (and large-dimension) geometric questions.
</p>
</dir>
</td>
<td bgcolor='#0B4C5F' style="color:white">
<dir>
<h4>Course Co-ordinator</h4>
Prof. Somenath Biswas<br>
Phone: 0532-2922027<br>
Email: sb {at} iiita {dot} ac {dot} in<br>
.......................................................<br>
Web: <a href='http://www.iiita.ac.in/administration/message_from_director/'>Profile page</a>
</dir>
</td></tr>
</table>
<br><br>
<h4>2. Evolution and Computation</h4>
<br>
<b>Preamble</b>
<br>
<p align='justify'>
Computer Science has largely evolved in an attempt to understand what computational models and resources can and cannot do. As the discipline matured, fascinating new models and problems arose which required novel and deep techniques to resolve. Many more questions are arising now as computational thinking is reaching beyond the traditional boundaries of computer science. Several fundamental problems in biology, physics, economics and the social sciences have turned out to be inherently computational and can be, and are being studied under the powerful “computational lens”, making use of the rich toolkit that computer science has developed over time.
</p>
<p align='justify'>
This course is one the first attempts to elaborate on the emerging connection between computer science and one of the key tenets in biology: evolution. Traditionally, the language of mathematical biology has been dynamical systems and Markov chains and these have been able model evolutionary processes locally. However, full understanding and rigorous analysis of these processes often require a global view. Algorithms provide such a view by making available a broader language and a richer set of tools. This course will illustrate how the algorithmic paradigm halps us explain and analyze certain evolution related biological phenomena.
</p>
<p align='justify'>
The objective of this course is to expose computer scientists to the richness of biology as an application area of the computational paradigm as well as to introduce the algorithmic methodology to mathematical biologists by providing examples of how certain evolution related problems have benefitted by applying the “computational lens” to them. The course should be of interest to CS students with an interest in biology, in particular, in evolution and to mathematical biologists interested in enriching their toolkit.
</p>
<p align='justify'>
The course is for 2 credits with 15 one-hour lectures and 5 one-hour tutorial/discussion sessions. Following topics will be covered under this course:
</p>
<table width='100%' border='1' style='border-collapse: collapse'>
<tr valign='top'>
<td bgcolor='#0B4C5F'><h4 style="color:white">Modules</h4>
</td><td width='70%'>
<ul>
<li>> Mathematical and algorithmic background (5 Lectures)
<dir>– Linear algebra,<br>
– Dynamical systems,<br>
– Probability and Markov chains,<br>
– Convex optimization.<br>
</dir>
</li>
<li>> Applications (10 Lectures, about 2 Lectures per topic below)
<dir>
– Linear models of evolution: Eigen’s quasispecies model and the Error threshold,<br>
– Quadratic models of evolution: Sex and language dynamics,<br>
– Stochastic finite population models of evolution: Wright-Fisher, Moran,<br>
– Algorithms as a product of evolution: the computational abilities of slime mold,<br>
– Instance as a product of evolution: computational aspects of protein folding.<br>
</dir>
</td></tr>
<tr valign='top'>
<td bgcolor='#0B4C5F'><h4 style="color:white">Who can attend...</h4></td>
<td>
<ul>
<li>> Faculties, Engineers, Scientists, and Researchers from academic, industrial and government organizations including R&D laboratories.
</li>
<li>> Students at all levels (BE/BTech/MSc/ME/MTech/PhD/Other) from academic and technical institutions/universities from India or abroad.
</li>
</ul>
</td></tr>
<tr valign='top'>
<td bgcolor='#0B4C5F'><h4 style="color:white">Fees</h4></td>
<td>
The participation fees for taking the course is as follows:
<dir>
Participants from abroad : US $500<br>
Industry/Research Organizations: INR 10,000<br>
Academic Institutions: INR 2,000 (half for SC/ST students)<br>
</dir>
<p align='justify'>
The above fee include all instructional materials, computer use for tutorials and assignments, laboratory equipment usage charges, 24 hr free internet facility. The participants will be provided with accommodation on payment basis.
</p>
</td></tr>
</table>
<br>
<h4>The Faculty</h4>
<table width='100%' cellpadding='20' border='1' style='border-collapse: collapse'>
<tr valign='bottom'>
<td width='70%'>
<dir>
<p align='justify'>
<img src='images/sb.jpg' width='50' height='50' />
<b><a href='http://www.iiita.ac.in/administration/message_from_director/'>Prof. Somenath Biswas</a></b> is the Director of Indian Institute of Information Technology Allahabad. He has been the President of the Indian Association for Research in Computing Science (2000-2002). His research interests are in the field of computational complexity theory, randomized algorithms, computational biology, logic in computer science.
</p>
<p align='justify'>
<img src='images/nv.jpg' width='50' height='50' />
<a href='http://people.epfl.ch/nisheeth.vishnoi'><b>Dr. Nisheeth Vishnoi</b></a> is currently an Associate Professor at the EPFL Lausanne, Switzerland. His research focuses both on foundational problems in algorithms, complexity, and optimization, and on how theoretical computer science can be used to gain insight into fundamental processes that occur in nature and society which are inherently computational.
</p>
</dir>
</td>
</dir>
</td>
<td bgcolor='#0B4C5F' style="color:white">
<dir>
<h4>Course Co-ordinator</h4>
Prof. Somenath Biswas<br>
Phone: 0532-2922027<br>
Email: sb {at} iiita {dot} ac {dot} in<br>
.......................................................<br>
Web: <a href='http://www.iiita.ac.in/administration/message_from_director/'>Profile page</a>
</dir>
</td></tr>
</table>
<br><br>
<h4>3. Modeling Cereberal Cortex and Plasticity</h4>
<br>
<b>Overview</b>
<p align='justify'>
To simulate computational cognition, it is necessary to understand the workings of brain and cognitive processes. The study of the development of the cortex and the precise orderly connections within and between cortical areas, which enable the processes that underlie sensation and perception, control of action, learning and memory etc., can lead to simulation of those processes on a computational system. These theoretical studies offer the prospect of connecting diverse research constructs and paradigms, and of providing a new understanding of the algorithms that drive our mental "machinery."
</p>
<p align='justify'>
The primary objectives of the course are as follows:
<ul>
<li>> Exposing participants to the fundamentals of functions and development of cerebral cortex,</li>
<li>> Application of tools and techniques in the field of computational neuroscience.</li>
<li>> Providing exposure to practical problems and their solutions, through demonstration of some computational models of cortex processing.</li>
</ul>
</p>
<table width='100%' border='1' style='border-collapse: collapse'>
<tr valign='top'>
<td bgcolor='#0B4C5F'><h4 style="color:white">Modules</h4>
</td><td width='70%'>
<ul>
<li>Lecture 1: Cortical Development: Early events<br>
Development of the cortex, Targettng and innervation of cortex, Interaction between ingrowing thalamic axons and the developing cortex
</li>
<li>Lecture 2: Activity-Dependent Development and Plasticity<br>
Activity-Dependent development of functions, Hebbian process, Synaptic efficacy during cortical development
</li>
<li>Lecture 3: Development of Interacortical Connections and Cortical Dynamics<br>
Functional segregation of processing streams, pattern of thalamocortical and intracortical projections in adults
<li>Lecture 4: Cortical Circuits and Computations<br>
Orientation and direction selectivity in visual cortex
</li>
<li>Lecture 5: Information Processing and Transfer in Visual Cortical Areas<br>
Information processing in the visual system, Vision modulation by voluntary attention
</li>
</ul>
</td></tr>
<tr valign='top'>
<td bgcolor='#0B4C5F'><h4 style="color:white">Who can attend...</h4></td>
<td>
<ul>
<li>> Faculties, engineers and researchers from academic, industrial and government organizations including R&D laboratories.
</li>
<li>> Students at all levels (BTech/MSc/MTech/PhD) from reputed academic and technical institutions.
</li>
</ul>
Number of participants for the course will be limited to fifty. Preference will be given to the participants opting against credits.
</td></tr>
<tr valign='top'>
<td bgcolor='#0B4C5F'><h4 style="color:white">Fees</h4></td>
<td>
The participation fees for taking the course is as follows:
<dir>
Participants from abroad : US $500<br>
Industry/Research Organizations: INR 10,000<br>
Academic Institutions: INR 2,000 (half for SC/ST students)<br>
</dir>
<p align='justify'>
The above fee include all instructional materials, computer use for tutorials and assignments, laboratory equipment usage charges, 24 hr free internet facility. The participants will be provided with accommodation on payment basis.
</p>
</td></tr>
</table>
<br><br>
<h4>The Faculty</h4>
<table width='100%' cellpadding='20' border='1' style='border-collapse: collapse'>
<tr valign='bottom'>
<td width='70%'>
<dir>
<p align='justify'>
<img src='images/ust.png' width='50' height='50' />
<a href='https://www.researchgate.net/profile/Uma_Shanker_Tiwary'><b>Prof. U.S. Tiwary</b></a> is Professor at Indian Institute of Information Technology Allahabad. His research interests are in the field of Image Processing, Computer Vision, Medical Image Processing, Pattern Recognition & Script Analysis, Digital Signal Processing, Speech and Language Processing, Wavelet Transform, Soft Computing & Fuzzy Logic, Neuro–computing and Soft-computers, Speech driven computers, Natural Language Processing, Brain Simulation, Cognitive Science.
</p>
<p align='justify'>
<img src='images/ms.jpg' width='50' height='50' />
<a href='http://bcs.mit.edu/users/msurmitedu'><b>Dr. Mriganka Sur</b></a> is the Paul E. and Lilah Newton Professor of Neuroscience and Director of the Simons Center for the Social Brain at MIT. Dr. Sur studies the organization, development and plasticity of the cerebral cortex of the brain using experimental and theoretical approaches. He has discovered fundamental principles by which networks of the cerebral cortex are wired during development and change dynamically during learning. His laboratory has identified gene networks underlying cortical plasticity, and pioneered high resolution imaging methods to study cells, synapses and circuits of the intact brain. Recently, his group has demonstrated novel mechanisms underlying disorders of brain development, and proposed innovative strategies for treating such disorders.
</p>
</dir>
</td>
<td bgcolor='#0B4C5F' style="color:white">
<dir>
<h4>Course Co-ordinator</h4>
Prof. U.S. Tiwary<br>
Phone: 0532-2922237<br>
Email: ust {at} iiita {dot} ac {dot} in<br>
.......................................................<br>
Web: <a href='https://www.researchgate.net/profile/Uma_Shanker_Tiwary'>Profile page</a>
</dir>
</td></tr>
</table>
<br><br>
<div align='right'><a href='http://www.gian.iitkgp.ac.in/ccourses/approvecourses2'>Source: http://www.gian.iitkgp.ac.in/ccourses/approvecourses2</a></div>
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