AI11002, 2025 Spring, IIT Kharagpur
Teaching, IIT Kharagpur, Department of AI, 2025
Teaching Distribution: Sole Instructor.
Logistics: In-person Mode, Weekly 3 hrs lecture + 1 hr Tutorial; Credits: 4
Students: 49 (Core subject for B-Tech in AI, First Year)
AI60203, 2024 Autumn, IIT Kharagpur
Teaching, IIT Kharagpur, Department of AI, 2024
Course Overview: Moodle Snapshot
Teaching Distribution: Sole Instructor.
Logistics: In-person Mode, Weekly 3 hrs lecture; Two sessions of Lab over the entire course; Credits: 3
Students: 125 (Core subject for M-Tech in AI + UG/PG students from all departments, selected based on CGPAs)
Student Feedback: 4.15/5 (IIT-KGP ERP Snapshot)
AI61003, 2024 Autumn, IIT Kharagpur
Teaching, IIT Kharagpur, Department of AI, 2024
Course Overview: Moodle Snapshot
Teaching Distribution: Taught till Mid-semester, i.e., till Stage 4: Optimization (followed up with Prof. Prabhat Kumar Mishra).
Logistics: In-person Mode, Weekly 3 hrs lecture + 1 hr tutorial; Credits: 4
Students: 164 (Core subject for M-Tech in AI + UG/PG students from all departments, selected based on CGPAs)
Student Feedback: 4.11/5 (IIT-KGP ERP Snapshot)
AI42001, 2024 Spring, IIT Kharagpur
Teaching, IIT Kharagpur, Centre of Excellence in AI, 2024
Course Overview: Moodle Snapshot
Teaching Distribution: Taught till Mid-semester, i.e., till Support Vector Machine (followed up with Prof. Somdyuti Paul and Prof. Sudheshna Sarkar).
Logistics: In-person Mode, Weekly 3 hrs lecture + 3 hrs Lab; Credits: 5
Students: 108 (UG/PG students from all departments, selected based on CGPAs)
Student Feedback: 4.32/5 (IIT-KGP ERP Snapshot)
AI61002, 2024 Spring, IIT Kharagpur
Teaching, IIT Kharagpur, Centre of Excellence in AI, 2024
Course Overview: Moodle Snapshot
Teaching Distribution: Taught after Mid-semester, Convolutional Neural Network (preceeded by Prof. Somdyuti Paul, followed up with Prof. Jiaul Hoque Paik).
Logistics: In-person Mode, Weekly 3 hrs lecture + 1 hr Tutorial; Credits: 4
Students: 137 (Senior UG/PG students from all departments, selected based on CGPAs)
Student Feedback: 4.05/5 (IIT-KGP ERP Snapshot)
IEEE Transactions on Image Processing
Undergrad Thesis, IIT Madras, EE Dept., 2020
Here, we identified a problem statement Deep Learning based models for Dual-lens Camera
, and designed a research plan for the same. Undergrad Nithin G K joined with me for implementation, which finally led to a TIP paper. Later, Nithin G. K. joined for Ph.D. under Prof. Vishal Patel, John Hopkins University, US.
ICCV-19 Oral
Undergrad Thesis, IIT Madras, EE Dept., 2019
Here, we identified a problem statement Motion Deblurring for Dual-lens Camera
, and designed a research plan for the same. Undergrad Sharath Girish joined with me for implementation, which finally led to an ICCV-19 Oral paper (4.3% acceptance rate). Later, Sharath Girish joined for Ph.D. under Prof. Rama Chellappa, University of Maryland, US.
Image Mosaicingfor Image Signal Processing Course
Graduate Course, IIT Madras, EE Dept., 2019
As a part of the Image Signal Processing Course offered by Prof. A. N. Rajagopalan, this lecture aims to make students understand the fundamentals of Image Mosaicing (i.e., the task of stitching images to create a panorama), and equip them to implement the same as an assignment. [Slides]
Traditional Image Processingand
Deep Learning based Motion Deblurring
Graduate Course, IIT Madras, EE Dept., 2019
As a part of the Deep Learning Course
offered by Prof. A. N. Rajagopalan and Prof. Kaushik Mitra, the first lecture (L1) aims to equip students from different branches to understand the basics of Image Processing, and the second lecture (L2) provides an overview of the Deep Learning solutions for the problem of Motion Deblurring. [Slides-L1] [Slides-L2]
Funded Project, IITM, EE Dept., 2018
Lots of accidents happen while exiting a vehicle from its side doors, for it is difficult to ensure whether another vehicle(s) are coming from behind and how fast it is. This project attempts to solve this issue; project associate Madumithra Krishnamoorthy was incharge. Together, we employ a side view camera and propose a deployable algorithm using image processing techniques to address this. Later, she joined as a graduate research student in image processing. [Demo]
the bestDeep Learning course-project
Graduate Course (Deep Learning), IITM, EE Dept., 2017
Here, I proposed a problem statement Efficient Sketch Classification for Resource-constrained platforms
, and designed a research plan for the same. Undergrads Akshit Kumar and Sachin Agrawal joined with me and finally got recognition for the best Deep Learning course-project. Presently, both the students are continuing research in Deep Learning; the former is a Master's student at the University of Michigan, US, and the latter is a Data Scientist at Daimler, Germany. [Slides]
Graduate Course, IIT Madras, EE Dept., 2017
When Deep Learning course
was first introduced in IITM, EE Dept. by Prof. A. N. Rajagopalan and Prof. Kaushik Mitra, I (along with Prof. A. N. Rajagopalan) prepared course contents and presentations for Convolutional Neural Network
. [Slides]
Deep Learning
Lab Talk, IIT Madras, EE Dept., 2015
This Lecture Series started at a time when Deep Learning
was taking off, and it aims to explain the (then) emerging field of Deep Learning for the (then) members of Image Processing and Computer Vision Lab and Computational Imaging Lab, headed by Prof. A. N. Rajagopalan and Prof. Kaushik Mitra, respectively.
Here, the first lecture introduces Deep Learning (L1), later, followed by a doubt-clearing session (L1.1). Lecture 2 covers Convolutional Neural Network (L2), and Lecture 3 provides a mathematical interpretation of Deep Learning; O Lost! This series is concluded with Lecture 4, which discusses several Deep Learning approaches for Image Processing. [Slides-L1] [Slides-L1.1] [Slides-L2] [Slides-L3] [Slides-L4]
