Course abstract
This will be an introductory graduate level course in neural networks for signal processing. It would be part-I of a III part series on neural networks and learning systems that the instructor intends to introduce and cover neural networks at the graduate level. The course starts with a motivation of how the human brain is inspirational to building artificial neural networks. The neural networks are viewed as directed graphs with various network topologies towards learning tasks driven by optimization techniques. The course deals with Rosenblatt’s perceptron, regression modeling, multilayer perceptron (MLP), kernel methods and radial basis functions (RBF), support vector machines (SVM), regularization theory and principal component analysis (Hebbian and kernel based). Towards the end, topics such as convolutive neural networks etc. that are based on the MLP basic topics will be touched upon. The course will have assignments that are theoretical and computer based working with actual data.
Course Instructor
Prof. Shayan Srinivasa Garani
Dr. Shayan Garani Srinivasa received his Ph.D. in Electrical and Computer Engineering from Georgia Institute of Technology \u2013 Atlanta, M.S. from the University of Florida \u2013 Gainesville and B.E. from Mysore University. Dr. Srinivasa has held senior engineering positions within Broadcom Corporation, ST Microelectronics and Western Digital. Prior to joining IISc, Dr. Srinivasa was leading various research activities, managing and directing research and external university research programs within Western Digital. He was the chairman for signal processing for the IDEMA-ASTC and a co-chair for the overall technological committee. He is the author of a book, several journal and conference publications, holds U.S patents in the area of data storage. Dr. Srinivasa is a senior member of the IEEE, OSA and the chairman for the Photonic Detection group within the Optical Society of America. His research interests include broad areas of applied mathematics, physical modeling, coding, signal processing and VLSI systems architecture for novel magnetic/optical recording channels, quantum information processing, neural nets and math modeling of complex systems.
Teaching Assistant(s)
Course Duration : Jul-Oct 2019
View Course
Enrollment : 15-May-2019 to 05-Aug-2019
Exam registration : 01-Jun-2019 to 30-Sep-2019
Exam Date : 17-Nov-2019
Enrolled
2201
Registered
57
Certificate Eligible
20
Certified Category Count
Gold
0
Silver
1
Elite
10
Successfully completed
9
Participation
6
Success
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Category : Successfully Completed
Elite
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Category : Elite
Silver
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Category : Silver
Gold
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Category : Gold
Legend
AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75 AND FINAL SCORE >=40
BASED ON THE FINAL SCORE, Certificate criteria will be as below:
>=90 - Elite + Gold
75-89 -Elite + Silver
>=60 - Elite
40-59 - Successfully Completed
Final Score Calculation Logic
- Assignment Score = Average of best 8 out of 12 assignments.
- FINAL SCORE (Score on Certificate) = 75% of Exam Score + 25% of Assignment Score.
Enrollment Statistics
Total Enrollment: -1
Registration Statistics
Total Registration : 57
Assignment Statistics
Exam score
Final score