Date	Topics	Readings
January 25: Lecture 1	Overview of topics: Introduction, Subspace Clustering.	Subspace Clustering for High Dimensional Data: A Review (Sections 1, 2, and 3).
February 1: Lecture 2	Ensemble Methods, Semi-supervised Learning and Clustering with Constraints.	Ensemble Based Systems in Decision Making (Pages 21-26). Constrained K-means Clustering with Background Knowledge Active Semi-Supervision for Pairwise Constrained Clustering (Active learning algorithm, Section 4) Background: how to compute a sample covariance matrix and examples
February 8: NO CLASS!	Class is cancelled due to inclement weather.
February 15: Lecture 3	First Quiz! Metric learning: Relevant Component Analysis; Neighbourhood Component Analysis; Large Margin Distance Metric Learning Homework 1. Due February 22.	Relevance Component Analysis Neighbourhood Components Analysis Distance Metric Learning for Large Margin Nearest Neighbor Classification
February 22: Lecture 4	Quiz Transfer learning.	Guidelines on the project and on the project term paper. A Survey on Transfer Learning. Boosting for Transfer Learning.
March 1: Lecture 5	Quiz Cross-domain Text Classification using Wikipedia, presented by Pu Wang. Kernel methods.	Cross-domain Text Classification using Wikipedia. A tutorial introduction on learning with kernels.
March 8: SPRING BREAK	Project proposal due.
March 15: Lecture 6	Quiz More on kernel methods. Kernels for text.	Chapter 10 of Kernel Methods for Pattern Analysis, by John Shawe-Taylor and Nello Cristianini
March 22: Lecture 7	LOF: Identifying Density-based Local Outliers, presented by Marc Brooks. Determining Word Sense Dominance using a Thesaurus, presented by Jared Mowery.	LOF: Identifying Density-based Local Outliers Determining Word Sense Dominance using a Thesaurus
March 29: Lecture 8	Revised project proposal due A Probabilistic Framework for Semi-Supervised Clustering, presented by Sam Blasiak. Creating Ensembles of Classifiers via Fuzzy Clustering and Deflection, presented by Tanwistha Saha.	A Probabilistic Framework for Semi-Supervised Clustering Creating Ensembles of Classifiers via Fuzzy Clustering and Deflection
April 5: Lecture 9	Quiz (kernel methods) Checkpoint: progress report for projects Dimensionality reduction and manifold learning: Principal Component Analysis ISOMAP and Locally Linear Embedding	A global geometric framework for nonlinear dimensionality reduction Think globally, fit locally: unsupervised learning of low dimensional manifolds
April 12: Lecture 10	Incremental Locally Linear Embedding Algorithm, presented by Sam Blasiak. Generalized Hebbian Algorithm for Incremental Singular Value Decomposition in Natural Language Processing, presented by Jared Mowery.	Incremental Locally Linear Embedding Algorithm Generalized Hebbian Algorithm for Incremental Singular Value Decomposition in Natural Language Processing
April 19: Lecture 11	Spectral learning, presented by Tanwistha Saha. Detection of emerging space-time clusters, presented by Marc Brooks.	Spectral learning Detection of emerging space-time clusters
April 26: Lecture 12	Density-Connected Subspace Clustering for High-Dimensional Data, reviewed by Marc and Jared. Information Theoretic Regularization for Semi-Supervised Boosting, reviewed by Tanwistha and Sam.	Density-Connected Subspace Clustering for High-Dimensional Data Information Theoretic Regularization for Semi-Supervised Boosting
May 3: Lecture 13	Project presentations: Tanwistha and Marc.	Guidelines on the project and on the project term paper.
May 12: 4:30-6:30pm	Project report due Project presentations: Jared and Sam.