Date	Topics	Readings
Aug 27: Lecture 1	Overview of Pattern Recognition, Model selection, Over-fitting, Classification and Regression examples. Review of Probability Theory (Sum rule and Product rule).	Slides on course organization Lecture 1's slides Review Learn more about the No Hands Across America project Chapter 1: Sections 1.1, 1.2, 1.3.
Sept 3: Lecture 2	Bayesian probabilities, ML estimation, MAP, Curse of dimensionality, Decision theory. Assignment 1	Lecture 2's slides Sections 1.4, 1.5.
Sept 10: Lecture 3	Linear models for classification, discriminant functions, Fisher's linear discriminant, Perceptron.	Lecture 3's slides Section 4.1. Project guidelines
Sept 17: Lecture 4	Probabilistic generative models; Probabilistic discriminative models Assignment 1 due Assignment 2 Download the Data	Lecture 4's slides Sections 4.2, 4.3
Sept 24: Lecture 5	Backpropagation	Lecture 5's slides Sections 5.1, 5.2, 5.3
Oct 1: Lecture 6	Principal Component Analysis Assignment 2 due	PCA - part 1 PCA - part 2 PCA - part 3 Sample Midterm Exam
Oct 8: Lecture 7	Midterm Clustering; Subspace Clustering	Lecture 7's slides
Oct 15: NO CLASS
Oct 22: Lecture 8	Guest lecture by Prof. Sean Luke: Online Training of Robots and Multirobot Teams Project proposal due
Oct 29: Lecture 9	Assignment 3 Kernel methods	Kernel methods
Nov 5: Lecture 10	Support Vector Machines	Handout
Nov 12: Lecture 11	Assignment 3 due Subspace Clustering (continued)	Subspace Clustering Paper on Locally Adaptive Clustering Tutorial on Multi-View Clustering
Nov 19: Lecture 12	Ensemble Methods; Parallel Spatial Boosting	Ensemble Methods Parallel Spatial Boosting (Slides) Parallel Spatial Boosting (Paper)
Nov 26: Lecture 13	Final (non-cumulative)
Dec 3: Lecture 14	Students' presentations: Yue Ning and Sumit Kawatra: Keyword Identification using Topic Modeling and a Graph Algorithm Bryan Absher and Keith Citrenbaum: Predictors as Categorization Engines Alan Jacobson: PCA + Clustering: An Algorithm for Text Document Categorization Nancy Egan Sharma: Learning Analytics: Analysis of Moodle logs to gain insight into learner behavior Xing Wang: Investigation on Shapelets Techniques in Time Series Classification Eric Yuan: Association Rule Mining of Undesired Behavior (ARMOUR) Zach Liebowitz: Analysis of Subspace Clustering Songrun Liu: Study on Clustering Curves Shenghui Zhou: People Detection in RGB-D Images
Dec 10: 7:30-10:15pm	Students' presentations: Brian Mitchell: Predicting baseball game attendance (and quantifying excitement) Steven Clark: Govea: Neural-networks as an aid to Monte-Carlo tree search in the game of go Coleen Davis: Unsupervised classification of hyper- and multi- spectral images Dan Sionov: Invoice image clustering: applying clustering techniques to highlight data on typical vendor invoices Evgueni Erchov: Automated technical analysis patterns recognition on S&P 500 data series Debdipto Misra: Classification of hand movements by observing ultrasound images of upper limbs Jeremy Smith: Gesture recognition using neural networks Michael Caldwell: What the K? J. Whitney O'Meara: Jump method for K-means clustering Habib Karbasian: Forecasting Political Conflict Using Hidden Markov Models Nikhil Muralidhar: Influence of temporal factors on recommendation quality