schedule
THIS IS A TENTATIVE SCHEDULE AND WILL BE UPDATED AS THE SEMESTER STARTS
LECTURE NOTES WILL BE UPDATED HERE
| TIMELINE | MATERIAL COVERED |
| WEEK 1 | Introduction, overview of basic statistics and image processing, statistical pattern recognition. Lecture Notes: Introduction, Probability Review. |
| Reading: DHS Chapter 1, Paper by Jain, A.K.; Duin, P.W.; Jianchang Mao, Statistical pattern recognition: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol 22, no 1, pp. 4-37, Jan. 2000. | |
| WEEK 2 | Bayes decision theory, two-category classification, minimum error-rate classification, decision surfaces and discriminant functions. Lecture Notes: Decisions. |
| Reading: DHS Chapter 2.1-2.6. | |
| WEEK 3 | Error probabilities and integrals, normal density and its discriminant functions. |
| Reading: DHS Chapter 2.7-2.8. In-Class Exercise: m-Files, Paper by P. Domingos and M. Pazzani, On the Optimality of the Simple Bayesian Classifier Under Zero-One Loss, Machine Learning, vol. 29, pp. 103-130, 1997. | |
| WEEK 4 | Discrete cases classification, independent features, supervised learning and parameter estimations, maximum likelihood estimation. Lecture Notes: Learning. In-Class Exercise Solution: m-Files. |
| Reading: DHS Chapter 2.9-2.10, 3.1-3.3, Assigned papers for presentation, Paper Presentation Pointers | |
| WEEK 5 | Bayes classifier and class-conditional densities, Bayesian learning, sufficient statistics. Lecture Notes: Learning II. |
| Reading: DHS Chapter 3.4-3.7, Assigned papers for presentation. | |
| WEEK 6 | Nonparametric techniques, Nearest neighbor estimations, Fisher's discriminant and multiple discriminant analysis. Lecture Notes: Learning III. |
| Reading: DHS Chapter 4.1-4.7. In-Class Exercise Solution: m-Files. | |
| WEEK 7 | Spring Break. |
| WEEK 8 | Research readings discussions. |
| Readings: DHS Chapter 3.8-3.9, assigned papers. | |
| WEEK 9 | Linear discriminant functions, generalized functions, minimum squared error procedures. Lecture Notes: Discriminant Functions. |
| Reading: DHS Chapter 5.1-5.8, Assigned papers for presentation. | |
| WEEK 10 | Unsupervised learning, maximum likelihood, similarity measures, clustering, dimensionality and its reduction. |
| Reading: DHS Chapter 10.1-10.11. | |
| WEEK 11 | Research readings discussions. |
| Reading: Assigned papers. | |
| WEEK 12 | Single layer networks, Multi-layer perceptrons, feed-forward mapping, weight-space symmetries, projection pursuit, back-propagation. |
| Reading: B Chapter 3.1-3.6, 4.1-4.10. | |
| WEEK 13 | Radial Basis functions, exact interpolation, training, regularization, regression, classification, comparison. |
| Reading: B Chapter 5.1-5.10. | |
| WEEK 14 | Final Project presentations. |