EE698V: Machine Learning for Signal Processing
Course Objectives:This course aims at introducing the students to the fundamentals of machine learning (ML) techniques useful for various signal processing applications. It will discuss various mathematical methods involved in ML, thereby enabling the students to design their own models and optimize them efficiently. The lectures will focus on mathematical principles, and there will be coding based assignments for implementation. Prior exposure to ML is not required. The course will be focused on applications in signal processing and communication, and the theory will be tailored towards that end.
References:This course will take excerpts from some standard books on machine learning and signal processing. But it will largely be based on articles and research papers in ML and SP conferences (e.g., ICASSP, NeurIPS, ICML, Interspeech, ISMIR, etc.) and journals (e.g., IEEE TASLP, JMLR, IEEE PAMI, IEEE Wireless Comm., etc.).
Reference Books:- "Pattern Recognition and Machine Learning", C.M. Bishop, 2nd Edition, Springer, 2011.
- "Deep Learning", I. Goodfellow, Y, Bengio, A. Courville, MIT Press, 2016.
- "Automatic Speech Recognition: A Deep Learning Approach", D. Yu and L. Deng, Springer, 2016.