Course Meeting Times

Lectures: 2 sessions / week, 1.5 hours / session

Recitations: 1 session / week, 1 hour / session

Topics Covered

  • Introduction 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, Feature Extraction
  • Eigenvector and Multilinear Analysis
  • Training Methods, Maximum Likelihood and Bayesian Parameter Estimation
  • Linear Discriminant/Perceptron Learning, Optimization by Gradient Descent
  • Support Vector Machines
  • K-Nearest-Neighbor Classification
  • Non-parametric Classification, Density Estimation, Parzen Estimation
  • Unsupervised Learning, Clustering, Vector Quantization, K-means
  • Mixture Modeling, Expectation-Maximization
  • Hidden Markov Models, Viterbi Algorithm, Baum-Welch Algorithm
  • Linear Dynamical Systems, Kalman Filtering
  • Bayesian Networks
  • Decision Trees, Multi-layer Perceptrons
  • Reinforcement Learning with Human Interaction
  • Genetic Algorithms
  • Combination of Multiple Classifiers "Committee Machines"



Homework/Mini-Projects, due every 1-2 weeks up until 3 weeks before the end of the term.

These will involve some programming (MATLAB®) assignments.

Project 30%
Midterm Quiz 25%
Your presence and interaction in lectures (especially your presence during the last two days of project presentations), in recitation, and with the staff outside the classroom 10%

Late Policy

Assignments are due by the start of 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.

Collaboration/Academic Honesty

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 as much as in undergraduate: what you learn is what matters. 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. Start early, and don't be disappointed if you get stuck when you try to do it solo; that frustrating experience can lead to more memorable and effective learning. 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. (This has happened to people before - it is not an empty threat.) If you team up on the final project (teams of two are encouraged), 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.


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