Intro to pattern recognition, feature detection, classification
Review of probability theory, conditional probability and Bayes rule
Random vectors, expectation, correlation, covariance
Review of linear algebra, linear transformations
Decision theory, ROC curves, Likelihood ratio test
Linear and quadratic discriminants, Fisher discriminant
Sufficient statistics, coping with missing or noisy features
Template-based recognition, eigenvector analysis, feature extraction
Training methods, Maximum likelihood and Bayesian parameter estimation
Linear discriminant/Perceptron learning, optimization by gradient descent, SVM
k-nearest-neighbor classification
Non-parametric classification, density estimation, Parzen estimation
Unsupervised learning, clustering, vector quantization, K-means
Mixture modeling, optimization by Expectation-Maximization
Hidden Markov models, Viterbi algorithm, Baum-Welch algorithm
Linear dynamical systems, Kalman filtering and smoothing
Bayesian networks, independence diagrams
Decision trees, Multi-layer Perceptrons
Combination of multiple classifiers "Committee Machines"

• Initial Information Available
• Data Available
• One Page Plan Due If Not Using Standard Data Set
• One Page Plan Due If Using Standard Data Set
• Project Presentations, Online Presentation Due
• Project Presentations Continued 

25% Midterm

10% Your presence and interaction in lectures (especially the last two days), in recitation, and with the staff outside the classroom.

Assignments are due by 5:00 p.m. on the due date, in the TA's office. You are also free to bring them to class on the due date. If you are late, you will get a zero on the assignment. However, the lowest assignment grade will be dropped in computing the final grade

The goal of the assignments is to help you learn, not to see how many points you can get. Grades in graduate school do not matter very much: what you learn really does matter. Thus, if you stumble across old course material with similar-looking problems, please try not to look at their solutions, but rather work the problem(s) yourself. Please feel free to come to the staff for help, and also to collaborate on the problems and projects with each other. Collaboration should be at the "whiteboard" level: discuss ideas, techniques, even details - but write your answers independently. This includes writing Matlab code independently, and not copying code or solutions from each other or from similar problems from previous years. If you are caught violating this policy it will result in an automatic F for the assignment AND may result in an F for your grade for the class. If you team up on the final project, then you may submit one report which includes a jointly written and signed statement of who did what. The midterm will be closed-book, but we will allow a cheat sheet.

The staff welcomes your comments on the course at any time. Please feel free to send us comments -- in the past, we have obtained helpful remarks that allow us to make improvements mid-course. We want to maximize the value of this course for everyone and welcome your input, positive or negative.

All students are expected to attend all project presentations the last two days of class; these tend to be very educational experiences, and thus attendance these last two days will contribute to your final grade.




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