DS 265o Deep Learning for Visual Analytics 3:1 (August 2022)
Course Instructor: Venkatesh Babu, CDS
Course description: In the recent years, Deep Learning has pushed to boundaries of research in many fields. This course focuses on the application of Deep Learning in the field of Visual Analytics. The course starts with the basics of Deep Learning, which are built on top of various concepts from Image Processing and Machine Learning. The second part of the course deals with the various flavors of Deep Learning in Computer Vision, such as Generative Models, Recurrent Models and Adversarial Robustness etc.
Syllabus
Basics of machine learning and computer vision, CNN basics, Loss function and back propagation, Object Recognition, Detection and Segmentation. Recurrent Neural Networks, LSTM, Generative Adversarial Networks (GANs), Self-supervised learning, Transformers, Explainable AI, Adversarial Robustness of Deep models.
Textbooks / References
- Dive into Deep learning, Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola (Online
- Recent research papers
Prerequisites: Basics knowledge of Machine learning and Image processing.
Grading:
- Projects 35%
- Assignments 25%
- Midterm 20%
- Final exam 20%.