MLSP 11-755/18-797

Date	Lecture Topics (Tentative)	Suggested Readings	Assignments
Aug 28	No Class
Aug 28 slides	Lecture 1: Introduction Introduction to course Notion of a signal Basic digital representation of data E.g. speech, images, other types of data	Foundations of Signal Processing. Ch 1.
Sep 3 slides	Lecture 2: Basics. Fundamentals of Linear Algebra, part 1 Vector Spaces Algebraic operations and their interpretations Projections	Linear Algebra Ch 1.3 Deep Learning Book. Ch 2.1-2.6
Sep 5 slides	Lecture 3: Basics Fundamentals of Linear Algebra, part 2 Basics of Calculus Types of operations Eigen decomposition, SVD Vector and Matrix Calculus Function Spaces	Linear Algebra Ch 6.1 Linear Algebra Ch 7.1 The Matrix Cookbook. Ch 2. Deep Learning Book. Ch 2.7-2.11	Quiz 1 Due Sep 8 Homework 1 out
Sep 10 slides	Lecture 4: Optimization Gradient Ascent/Descent Basics of Convex Optimization Constrained Optimization Lagrange multipliers Projected gradients	Convex Optimization Ch 9
Sep 12 slides	Lecture 5: Roger Dannenberg Music Understanding		Quiz 2 Due Sep 15
Sep 17 slides	Lecture 6: Representation Deterministic Representations Wavelets Fourier transforms Other Deterministic Transforms	DFT Wavelets and Signal Processing Time frequency Representation
Sep 19 slides	Lecture 7: Representations Data-driven Representations Eigen representations Karhunen-Loeve PCA	Eigenfaces for Recognition Tutorial on PCA	Quiz 3 Due Sep 22 Homework 1 due
Sep 24 slides	Lecture 8: Classification Introduction to Classification Binary Classification Boosting Application to Face Detection	Statistical Learning. Ch 10
Sep 26 slides	Lecture 9: Classification Face Detection Integral Image Cascade Classifier Pratical Implementation	Viola-Jone's paper	Quiz 4 Due Sep 29
Oct 1 slides	Lecture 10: Representations Data-driven representations Independent Component Analysis ICA for representations and denoising ICA applications	ICA, new concept? ICA, algorithms and applications
Oct 3	Lecture 11 (part 2): Representations Data-driven representations Independent Component Analysis Information Theoretical based Methods Applications	Spectrogram dimensionality reduction with independence constraints	Quiz 5 Due Oct 6
Oct 8 slides	Lecture 12: Representations Data-driven representations Non-negative matrix factorization Types of NMF Overcomplete representations Sparsity Applications	Algorithms for NNMF Speech Denoising using NNMF with prios	Homework 2 due
Oct 10 slides	Lecture 13: Modelling/Representations Clustering Basic idea K-means Bag of Words Kernels and Mercer's condition Kernel K-means	Spectral Clustering Efficient ASD prediction with eye movement	Quiz 6 Due Oct 13
Oct 15 slides	Lecture 14: Shinji Watanabe End-to-End Speech Processing: From Pipeline to Integrated Architecture
Oct 17 slides	Lecture 15: Representations/Modelling Dictionary based representations Sparse and overcomplete representations Application to denoising	CoSAMP Sparse Modeling of Signals and Images	Quiz 7 Due Oct 20
Oct 22 slides	Lecture 16: Vishwanath S R V Compressive Sensing
Oct 24 slides	Lecture 17: Prediction and Modeling Nearest neighbors Linear regression Kernel regression Regularization Tikhonov and L1 regularization Sparsity	Locality Sensitive Hashing Random Features for Large Scale Kernel Machines	Quiz 7 Due Oct 20
Oct 29 slides	Lecture 18: Classification Linear classifiers Perceptrons Margin perceptrons SVM	Statistical Learning. Ch 4.
Oct 31 slides	Lecture 19: Modelling Statistical modelling ML estimation Expectation Maximization Gaussian Mixture Models	Event Detection using GMM
Nov 5 slides	Lecture 20: Classification Bayes classification Naive Bayes Max-entropy Modeling	Statistical Learning. Ch 6.	Quiz 9 Due Nov 3
Nov 7 slides	Lecture 21: Supervised Representations Distinction between supervised and unsupervised models CCA LDA	CCA-based acoustic features for phonetic recognition
Nov 12 slides	Lecture 22: Modelling MAP Estimation Linear Gaussian Models	Voice biometrics using linear Gaussian Model	Quiz 9 Due Nov 10
Nov 14 slides	Lecture 23: Factor Analysis
Nov 19	Lecture 25: Michael Sipe Guest Lecturer
Nov 21 slides	Lecture 24: Modelling/Classification Markov Models Hidden Markov Models Training HMMS Classification with HMMs Segmentation with HMMs	Tutorial on HMM	Quiz 10 Due Nov 17
Nov 26 slides	Lecture 24: Modelling/Classification Markov Models Hidden Markov Models Training HMMS Classification with HMMs Segmentation with HMMs	Tutorial on HMM	Quiz 10 Due Nov 17
Nov 28	Thanksgiving No Class
Dec 3 slides	Lecture 26: Modelling/Prediction Linear dynamic models Kalman filters Extended Kalman filters	A tutorial introduction to Kalman filtering Nonlinear filters: beyond the Kalman filter	Quiz 11 Due Nov 24
Dec 5	Poster Sessions Location: TBD

Machine Learning for Signal Processing

11-755 / 18-797 Fall 2019

Prof. Bhiksha Raj