RIT Department of Computer Science

RIT Academic Calendar

---

Week	Topics	Due Dates
Classification
1	Overview, Nearest Neighbor Classification
2	Bayesian Decision Theory and Linear Classifiers	A1 due
3	Decision Trees
4	Ensembles: AdaBoost, Random Forests	A2 due
5	Ensembles, continued
6	Support Vector Machines	A3 due
Segmentation
7	Dimensionality Reduction (PCA, LDA)	Project 1 (Classification) due
8	Segmentation overview
9	Segmentation, continued	A4 due
10	Clustering (including k-means)
Parsing
11	Syntactic and Structural Pattern Recognition	Project 2 (Segmentation) due
12	Hidden Markov Models
13	Stochastic Context-Free Grammars	A5 due
14	Parsing, continued
15	Review and Project Presentations	Project 3 (Parsing) due
16	Final Exam	(Date and location TBA)

---

Readings: this course makes use of a variety of texts, including Pattern Classification (Duda, Hart and Stork), Elements of Statistical Learning (Hastie, Tibshirani and Friedman), Pattern Recognition and Machine Learning (Bishop), Boosting (Freund and Schapire), C4.5 (Quinlan), research papers, and other sources. I try to use the clearest introduction to each topic that I know of.