2007 Spring Semester MAS 557 Lecture Plan

lecture #01(2.27) : introduction (syllabus), definition of machine learning (ML1.1)

lecture #02(3.06) : learning systems (ML1.2), issues in machine learning (ML1.3)

lecture #03(3.08) : concept learning (ML2.1-2.4), version space and C.E. algorithm (ML2.5-2.7),

                 (homework #1)

lecture #04(3.13) : decision tree (ML3.1-3.2), decision tree learning algorithms (ML3.3-3.4)

lecture #05(3.15) : hypothesis space search in decision tree (ML3.5), issues (ML3.6-3.7),

                 (homework #2)

lecture #06(3.20) : Perceptron for classification (NN3)

lecture #07(3.22) : Perceptron for regression I

lecture #08(3.27) : Perceptron for regression II, (homework #3)

lecture #09(3.29) : supervised learning algorithms

lecture #10(4.03) : parametric estimation I (PC3)

lecture #11(4.05) : parametric estimation II, (homework #4)

lecture #12(4.10) : nonparametric estimation (PC4)

lecture #13(4.12) : reserved

--- Mid-Term Exam ---

lecture #14(4.24) : MLP (NN4)

lecture #15(4.26) : RBF and function approximation (NN5)

lecture #16(5.01) : recurrent neural networks (NN14), (homework #5)

lecture #17(5.03) : estimating hypothesis accuracy (ML5.1-5.4)

lecture #18(5.08) : comparing learning algorithms (ML5.4-5.6), (homework #6)

lecture #19(5.10) : PAC learning (ML7.1-7.3)

lecture #20(5.15) : VC dimension (ML7.4, LD4.1-4.2), mistake bound (ML7.5)

lecture #21(5.17) : bounds on the generalization (LD4.3), structural minimization (LD4.4-4.5),

                (homework #7)

lecture #22(5.22) : SVM (LD9.1-9.4)

lecture #23(5.29) : Bayesian decision theory (ML6)

lecture #24(6.07) : Bayesian belief network, (homework #8)

lecture #25(6.09) : Presentations

lecture #26(6.14) : Presentations

--- Final Exam ---