Graduate level course
Course Description:
Deep Learning has gained a lot of popularity due to its recent breakthrough results in many real-world applications such as speech recognition, machine translation, image understanding, and robotics. The primary idea of deep learning is to build high-level abstractions of the data through multi-layered architectures. This course introduces the fundamental principles, algorithms, and applications of deep learning. It will provide an in-depth understanding of various concepts and popular techniques in deep learning. This course is mainly designed for graduate students who are interested in studying deep learning techniques and their practical applications. Basic knowledge and understanding of machine learning and data analytics algorithms are required.
The course begins with a thorough treatment of deep feedforward networks along with various regularization and optimization techniques used for efficiently learning these models. Different forms of the network architectures such as convolutional networks, recurrent neural networks, and autoencoders will be discussed in detail. Other advanced concepts such as deep generative models and deep reinforcement learning will also be covered. Finally, the course will conclude with a discussion on few real-world application domains where deep learning techniques have produced astonishing results.
#Topics Covered:
Topics covered in the course
Books: The material for this course will be adapted from a wide range of sources. While there is no single textbook that will be used in this course, the students might find the following books to be useful.
- Charu C. Aggarwal, "Neural Networks and Deep Learning: A Textbook", Springer, September 2018.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville, "Deep learning", MIT Press, 2016.
- Aston Zhang and Zachary C. Lipton and Mu Li and Alexander J. Smola, Dive into Deep Learning (https://d2l.ai/ (Links to an external site.)), 2020.
- Simon O. Haykin, "Neural Networks and Learning Machines", Pearson Education, 3rd Edition, 2008.