Course
PostgraduateSemester
ElectivesSubject Code
AVD870Subject Title
Deep Learning for Computational Data ScienceSyllabus
Introduction of deep learning- Foundations of deep learning, basics aspects of machine learning, artificial intelligence, mathematics, statistics, and neurosciences (both theory and applications)- Applications in self-driving cars, new kinds of video games, AI, Automation, object detection and recognition, surveillance tracking etc.- Introduce to Neural networks and Deep learning approaches (mainly Convolutional Neural networks) and give a few typical applications.
CNN, RNN, GAN, VAE and transformer based architecture- for application to problem domains like speech recognition and computer vision.
Text Books
Same as Reference
References
1. Deep Learning, Ian Good fellow, Yoshua Bengio and Aaron Courville, ISBN-13:978- 0262035613, MIT Press, 2016.
2. Pattern Classification, Duda,R.O., Hart,P.E., and Stork,D.G. Wiley-Interscience. 2nd Edition, 2001.
3. Pattern Recognition, The odoridis, S. and Koutroumbas, K. 4th Edition. Academic Press, 2008.
4. Artificial Intelligence: A Modern Approach. Prentice-Hall Series in Artificial Intelligence, Russell,S. and Norvig,N., 2003.
5. Neural Networks for Pattern Recognition, Bishop,C.M. Oxford University Press, 1995.
6. The Elements of Statistical Learning, Hastie,T., Tibshirani, R.and Friedman,J. Springer, 2001.
7. Probabilistic Graphical Models, Koller,D. and Friedman, N. MIT Press, 2009.