Announcements:
June 10: Usebackpack.com will be used to share resources such as lecture slides and demos, and collect project reports and codes.
Course Objective:
The course will cover advanced digital signal processing topics mainly targeted at state-of-the-art applications in artificial vision and machine learning.
Reference Books & Resources:
- Digital Signal Processing: Principles, Algorithms and Applications by John G. Proakis, Dimitris G. Manolakis
- Neural Networks: A Comprehensive Foundation by Simon Haykin
- Wavelet Transforms: Introduction to Theory and Applications by Raghuveer M. Rao and Ajit S. Bopardikar
- Fundamentals of Nonlinear Digital Filtering by Jaakko Astola and Pauli Kuosmanen
- Nonlinear Digital Filters by Ioannis Pitas and Anastasios N. Venetsanopoulos
- Digital Image Processing by William K. Pratt
- Digital Image Processing by R. Gonzalez and R. Woods
- Latest Research Papers and other online resources in Deep Learning for Artificial Vision*
* Will be discussed in class.
Class Information:
Class Timings - Wednesday 12.00 - 12.55 HRS, Thursday 11.00 - 11.55 HRS and Friday 9.00 to 10.55 HRS
Venue - F-300, Electronics & Electrical Communication Engineering Building.
Office hours - Friday – 12 noon to 2 pm.
Teaching Assistants:
Sobhan Kanti Dhara
Ashish Verma
Debanjan Sengupta
* Contacts will be shared in Usebackpack.com
Course Syllabus:
(Detailed in Usebackpack.com)
Discrete linear time/space invariant systems and convolution
Artificial neural networks
Deep learning: Convolutional neural networks
Unitary signal transforms
Multirate DSP and signal pyramids
Wavelet series and transform
Nonlinear signal processing
* The above topics will include their state-of-the-art applications and demos.
Evaluation:
TA Component: 20% (fixed by institute)
Performance in Project – 20%
Midsem Component: 30% (fixed by institute)
Midsem Project Evaluation – 10%
Pre-Midsem Test – 5%
Midsem Exam# – 15%
Endsem Component: 50% (fixed by institute)
Endsem Project Evaluation – 20%
Pre-Endsem Test – 5%
Endsem Exam# – 25%
*Parts of the entire project component (50% of total) will be elaborated in class.
#Attendance must for consideration of the entire (Midsem/Endsem) component
Lecture Resources: (will be posted in usebackpack.com after the lecture is done)
Usebackpack Link: https://www.usebackpack.com/iitkgp/courses/badb76