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IIT Hyderabad Free Deep Learning with Computer Vision Course | IIT Certificate | AICTE Free Courses

Hi everyone IIT Hyderabad has launched a Free Deep Learning with Computer Vision Course on the platform of SWAYAM. You will get access to an 12-week free online course with assignments and at last, an exam which you have to clear in order to get the certificate from AICTE and IIT Hyderabad. So do not waste this opportunity, get all the details below and apply.

This course will introduce the students to traditional computer vision topics, before presenting deep learning methods for computer vision. The course will cover basics as well as recent advancements in these areas, which will help the student learn the basics as well as become proficient in applying these methods to real-world applications. The course assumes that the student has already completed a full course in machine learning, and some introduction to deep learning preferably, and will build on these topics focusing on computer vision.

INTENDED AUDIENCE: Senior undergraduate students + post-graduate students
PREREQUISITES

  • Completion of a basic course in Machine Learning
  • (Recommended, but not mandatory) Completion of a course in Deep Learning, or exposure to topics in neural networks
  • Knowledge of basics in probability, linear algebra, and calculus
  • Experience of programming, preferably in Python

If you are unsure whether you meet the background requirements for the course, please look at Assignment 0 (both theory and programming). If you are comfortable solving/following these assignments, you are ready for the course.

INDUSTRIES SUPPORT: All companies that use computer vision for their products/services (Microsoft, Google, Facebook, Apple, TCS, Cognizant, L&T, etc)
Summary

Course Status:Upcoming
Course Type:Elective
Duration:12 weeks
Start Date:26 Jul 2021
End Date:15 Oct 2021
Exam Date:24 Oct 2021
Enrollment Ends:02 Aug 2021
Category:Computer Science and EngineeringArtificial IntelligenceData Science
Credit Points:3
Level:Undergraduate/Postgraduate

Course layout
Week 1: Introduction and Overview:
○ Course Overview and Motivation; Introduction to Image Formation, Capture and Representation; Linear Filtering, Correlation, Convolution
Week 2: Visual Features and Representations:
○ Edge, Blobs, Corner Detection; Scale Space and Scale Selection; SIFT, SURF; HoG, LBP, etc.
Week 3: Visual Matching:
○ Bag-of-words, VLAD; RANSAC, Hough transform; Pyramid Matching; Optical Flow
Week 4: Deep Learning Review:
○ Review of Deep Learning, Multi-layer Perceptrons, Backpropagation
Week 5: Convolutional Neural Networks (CNNs):
○ Introduction to CNNs; Evolution of CNN Architectures: AlexNet, ZFNet, VGG, InceptionNets, ResNets, DenseNets
Week 6: Visualization and Understanding CNNs:
○ Visualization of Kernels; Backprop-to-image/Deconvolution Methods; Deep Dream, Hallucination, Neural Style Transfer; CAM,Grad-CAM, Grad-CAM++; Recent Methods (IG, Segment-IG, SmoothGrad)
Week 7: CNNs for Recognition, Verification, Detection, Segmentation:
○ CNNs for Recognition and Verification (Siamese Networks, Triplet Loss, Contrastive Loss, Ranking Loss); CNNs for Detection: Background of Object Detection, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, RetinaNet; CNNs for Segmentation: FCN, SegNet, U-Net, Mask-RCNN
Week 8: Recurrent Neural Networks (RNNs):
○ Review of RNNs; CNN + RNN Models for Video Understanding: Spatio-temporal Models, Action/Activity Recognition
Week 9: Attention Models:
○ Introduction to Attention Models in Vision; Vision and Language: Image Captioning, Visual QA, Visual Dialog; Spatial Transformers; Transformer Networks
Week 10: Deep Generative Models:
○ Review of (Popular) Deep Generative Models: GANs, VAEs; Other Generative Models: PixelRNNs, NADE, Normalizing Flows, etc
Week 11: Variants and Applications of Generative Models in Vision:
○ Applications: Image Editing, Inpainting, Superresolution, 3D Object Generation, Security; Variants: CycleGANs, Progressive GANs, StackGANs, Pix2Pix, etc
Week 12: Recent Trends:
○ Zero-shot, One-shot, Few-shot Learning; Self-supervised Learning; Reinforcement Learning in Vision; Other Recent Topics and Applications

CRITERIA TO GET A CERTIFICATE

Average assignment score= 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score= 75% of the proctored certification exam score out of 100

Final score= Average assignment score + Exam score

The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 24 October 2021 Morning session 9 AM to 12 Noon; Afternoon Session 2 PM to 5 PM.
Registration url: Announcements will be made when the registration form is open for registrations.

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup. It will have the logos of NPTEL and IIT Hyderabad.

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