RIT Academic Calendar
Week Topics Assign/Proj Presentations   Classification 1 Overview, Nearest Neighbor Classification 2 Bayesian Decision Theory and Linear Classifiers 3 Dimensionality Reduction (PCA, LDA) A1 due 4 Support Vector Machines Class. Pres. #1 *W/T: Career Fair 5 Ensembles: AdaBoost, Random Forests A2 due; Proj. 1 assigned 6 Ensembles: CNN and 'Deep' Neural Nets Class. Pres. #2 Segmentation 7 Segmentation + Clustering (incl. k-means) Proj. 1 due 8 Segmentation, continued Proj. 2 assigned Seg. Pres. #1 9 [ -- Spring Break -- ] 10 Object Detection: Joint Class. + Seg. A3 due Seg. Pres #2 11 Segmentation, continued Proj. 2 due Parsing 12 Parsing Overview, Structural Pattern Rec. Proj. 3 assigned Parsing Pres. #1 13 Sequences: HMM, LSTM/BLSTM A4 due Parsing Pres. #2 14 Hierarchies: Stochastic Context-Free Grammars Proj. 3 due 15 Parsing, Cont'd. Project Presentations 16 Class Monday; -- Exam Week -- A5 due
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), along with research papers and other sources. Readings will be provided through MyCourses.