Machine Learning for Signal Processing

E9 205 • Spring 2021

Announcements Syllabus Grading Textbooks Slides

Announcements

Enrollment Form

MLSP21 Enrollment Form

Link

Location

Microsoft Teams

Link

Logistics

Instructor

Dr. Sriram Ganapathy

sriramg@iisc.ac.in

Office: C 334 (2nd Floor)

Class Times

Mon & Wed

3:30 PM – 5:00 PM

Microsoft Teams: Link

Lab: Spectrum Lab (EE 1st Floor)

Teaching Assistants

Nareddy Kartheek Reddy

nareddyreddy aT iisc doT ac doT in

Syllabus

  • •Introduction to real world signals - text, speech, image, video.
  • •Feature extraction and dimensionality reduction - principal components, linear discriminants.
  • •Decision theory for pattern recognition, ML and MAP methods, Bias-variance trade-off, model assessment, cross-validation, estimating generalization error.
  • •Generative modeling and density estimation - Gaussian and mixture Gaussian models, kernel density estimators, hidden Markov models. Expectation Maximization.
  • •Linear regression and kernel methods. Regularization methods.
  • •Discriminative modeling - support vector machines, decision trees and random forest classifiers, bagging and boosting.
  • •Neural networks: gradient descent optimization and back propagation, regularization in neural networks, dropout, normalization methods.
  • •Introduction to deep learning - feedforward, convolutional and recurrent networks, practical considerations in deep learning.
  • •Introduction to graphical models - directed and undirected graphs, belief propagation.

Grading Details

25%
Assignments
20%
Midterm Exam
20%
Project
35%
Final Exam

Pre-requisites

Must - Random Process/Probablity and Statistics
Must - Linear Algebra/Matrix Theory

Textbooks

B1

Pattern Recognition and Machine Learning

C.M. Bishop, 2nd Edition, Springer, 2011.

B2

Neural Networks

C.M. Bishop, Oxford Press, 1995.

B3

Machine Learning: A Probabilistic Perspective

K. Murphy, 2012.

B4

Deep Learning

I. Goodfellow, Y. Bengio, A. Courville, MIT Press, 2016.

HTML Version

References

Deep Learning : Methods and Applications

Li Deng, Microsoft Technical Report.

Automatic Speech Recognition - Deep learning approach

D. Yu, L. Deng, Springer, 2014.

Machine Learning for Audio, Image and Video Analysis

F. Camastra, Vinciarelli, Springer, 2007.

PDF
Various Published Papers and Online Material
Python Programming Basics
PDF

Slides

01-03-2021
Introduction to real world signals - text, speech, image, video. Learning as a pattern recognition problem. Examples. Roadmap of the course.
Download Slides