Summer Training on Regression and Deep Learning








About the Training

In this training, we will be going over the basics of regression and deep learning. We will start with linear regression, and then consider logistic regression. We will move on to artificial neural networks and deep learning. The focus will be on the underlying concepts, mathematics, and algorithms.

Schedule

11.06.2018:  (Day:1)
8:30 AM : Registration-kit collection, tea, and networking
9:20 AM : A brief introduction to SAU & overview of RDL'18
9:30 AM : Technical Session-1
12:15 PM : Lunch break
1:30 PM : Technical Session-2
4:00 PM : Tea break and networking
4:30 PM onwards: Interactions with Data Science & Analytics research group members

12.06.2018:  (Day:2)
9:00 AM : Tea and networking
9:30 AM : Technical Session-3
12:00 Noon : Discussion with participants
1:00 PM : Lunch break & Certificate/Fee-receipt collection

Syllabus

1. Linear Regression: Ordinary least squares, multiple regression, kernel regression, L1 regression
2. Logistic Regression: binary and multi-class regression
3. Neural networks: Multilayer perceptrons (MLPs), backpropagation
4. Recurrent Neural Networks (RNNs): RNNs, backpropagation in time, bidirectional RNNs
5. Gated RNNs: Long short-term memory (LSTM), gated recurrent units (GRU)
6. Convolutional Neural Networks (CNNs): convolutions, activations, deep CNNs
7. Evaluation: regression modelling, assessment

Pre-requistes

It assumes some familiarity with linear algebra, probability and statistics.

References

1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep learning, MIT Press, 2016.
2. Mohammed J. Zaki, Wagner Meira, Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2014.

Who Can Apply

Industry professionals, Faculty members, research scholars, and post graduate students from universities/ colleges/ institutes who are either working or interested in the fields of data analytics, deep learning or machine learning are eligible to apply.

About Speaker

Mohammed J. Zaki is a Professor of Computer Science at RPI. He received his Ph.D. degree in computer science from the University of Rochester in 1998. His research interests focus on developing novel data mining and machine learning techniques, especially for applications in text mining, social networks and bioinformatics. He has over 250 publications, including the Data Mining and Analysis textbook published by Cambridge University Press, 2014. He is the founding co-chair for the BIOKDD series of workshops. He is currently an associate editor for Data Mining and Knowledge Discovery, and he has also served as Area Editor for Statistical Analysis and Data Mining, and an Associate Editor for ACM Transactions on Knowledge Discovery from Data, and Social Networks and Mining. He was the program co-chair for SDM'08, SIGKDD'09, PAKDD'10, BIBM'11,CIKM'12, ICDM'12, IEEE BigData'15, and CIKM'18. He is currently serving on the Board of Directors for ACM SIGKDD. He received the National Science Foundation CAREER Award in 2001 and the Department of Energy Early Career Principal Investigator Award in 2002. He received an HP Innovation Research Award in 2010, 2011, and 2012, and a Google Faculty Research Award in 2011. He is an ACM Distinguished Scientist and a Fellow of the IEEE. His research is supported in part by NSF, NIH, DOE, IBM, Google, HP, and Nvidia.
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About Coordinator

Dr. Muhammad Abulaish is an Associate Professor at the Department of Computer Science, South Asian University with over 17 years of experience in Academic and Research. Before joining South Asian University in Feb. 2016, he served Jamia Millia Islamia (A Central University) for more than 12 years (including 3 years as the head of the Department of Computer Science); Hamdard University for the period Sep. 2000 to Dec. 2003; and UPTEC Computer Consultancy Ltd., Lucknow for the period June 1998 to Aug. 2000. He was also the head of the "Internet Surveillance and Forensics" research group at the Centre of Excellence in Information Assurance, King Saud University, Saudi Arabia during Oct 2010 to Dec. 2012. Abulaish received his Masters degree, MCA, from Motilal Nehru National Institute of Technology Allahabad in 1998, and PhD degree in Computer Science from Indian Institute of Technology Delhi in Feb. 2007. Abulaish's research interests are in the areas of Data Analytics and Mining, Social Computing, Web Intelligence, Predictive Modeling, and Sentiment Analysis. He is keenly interested in developing analytical frameworks for integrated analysis of unstructured and structured data using data mining techniques for varied applications, including business intelligence, social network analysis, cyber security and forensics, open source intelligence, and web surveillance. Abulaish has over 75 research publications, including 3 papers in IEEE Transactions.
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About South Asian University

South Asian University (SAU) is an international university established by the eight member nations of South Asian Association for Regional Co-operation (SAARC) viz. Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. SAU started its operations from the academic year 2010. The university offers post-graduate and doctoral programmes in various disciplines that include Economics, Computer Science, Biotechnology, Mathematics, Sociology, International Relations and Law. It has 11 post-graduate faculties and a faculty of undergraduate studies. SAU attracts students from all member nations and its degrees are recognized by all the eight SAARC countries.
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