## EE698V: Machine Learning for Signal ProcessingCOURSE OUTLINE is available here ## 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.## Topics:- Linear Algebra Refresher
- Probability Theory Refresher
- Digital Signal Processing Refresher
- Machine Learning basics
- Supervised and Unsupervised learning
- Classification and Regression (linear models)
- Evaluation metrics
- Probability Models and Expectation Maximization Algorithm
- Gaussian Mixture Models
- Neural Networks and Deep Learning
- Multi-class classification and Multi-label classification
- Different kinds of non-linearities, objective functions and learning methods
- ML for Audio Classification
- Time Series Analysis, LSTMs and CNNs
- ML for Speech Recognition
- Hidden Markov Models, Finite State Transducers and Dynamic Programming
- ML for Music Information Retrieval
- Latent Variable Models, Matrix Factorization and Signal Separation
- ML for Image Processing
- Transfer Learning, Attention models, Attribute-based learning
- ML for Communication
- Deep learning for wireless applications
## 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. |