| When | MW 3:30 - 5:00 pm |
| Where | Microsoft Teams |
| Who | Sriram Ganapathy |
| Office | C 334 (2nd Floor) |
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| Teaching Assistant | Debarpan Bhattacharya, Praneeth Linga Reddy Vonteddu, Hariom Choudhary |
| Lab | LEAP Lab (EE C328 2nd Floor) |
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Announcements
- MLSP22 Enrollment Form link
- Location - Microsoft Teams
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
| Assignments | 25% |
| Midterm exam. | 20% |
| Final exam. | 35% |
| Project | 20% |
Pre-requisites
- Must - Random Process/Probablity and Statistics
- Must - Linear Algebra/Matrix Theory
Textbooks
- “Pattern Recognition and Machine Learning”, C.M. Bishop, 2nd Edition, Springer, 2011.
- “Neural Networks”, C.M. Bishop, Oxford Press, 1995.
- "Machine Learning: A Probablistic Perspective:, K. Murphy, 2012.
- “Deep Learning”, I. Goodfellow, Y, Bengio, A. Courville, MIT Press, 2016. html
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
| 05-01-2022 | Introduction to real world signals - text, speech, image, video. Learning as a pattern recognition problem. Examples. Roadmap of the course. |
slides |