Machine Learning Fall 2015 Schedule
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SessionDateTopicsReading (Ranges are inclusive)Assignments
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1Tue 8/25Introduction and OverviewMurphy 1-2 or Barber 1, 13.1HW0
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2Thu 8/27Model Selection and Types of Machine LearningMurphy 3.5 or Barber 10.1-10.3HW0 due. HW1 out
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3Tue 9/1Naive BayesMurphy 16.2 or Barber 7.2 or Mitchell https://www.cs.princeton.edu/courses/archive/spring07/cos424/papers/mitchell-dectrees.pdf, and http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
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4Thu 9/3Decision TreesMurphy 8.1-8.3 or Barber 17.4-17.4.1
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5Tue 9/8Logistic RegressionMurphy 8.5
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6Thu 9/10PerceptronHW1 due. HW2 out
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7Tue 9/15Neural Networks
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8Thu 9/17Intro to OptimizationMurphy 16.5-16.5.4, Rojas 7-7.3 http://page.mi.fu-berlin.de/rojas/neural/
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9Tue 9/22Neural networks II, backpropagationMurphy 14.5 or Barber 17.5
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10Thu 9/24Support vector machinesMurphy 14.1-14.2 or Barber 17.4HW2 due. HW3 out
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11Tue 9/29Duality and optimization, kernels
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12Thu 10/1Kernels continued
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13Tue 10/6
SVM variants (SMO, stochastic SVM)
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14Thu 10/8Linear and kernel regressionHW3 due (on Saturday)
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15Tue 10/13Midterm Review
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16Thu 10/15Midterm Exam (in class)HW4 out
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17Tue 10/20Principal Components AnalysisMurphy 12.2.1-12.2.3
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18Thu 10/22Clustering I (mixture models)
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19Tue 10/27
Clustering II (variational EM and k-means)
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20Thu 10/29Bayesian networks
HW4 due (on Saturday). HW5 out, start working on project
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21Tue 11/3Hidden Markov models IMurphy 17-17.4.3.3
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22Thu 11/5Hidden Markov models IIMurphy 17.5-17.5.2
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23Tue 11/10Markov random fields I
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24Thu 11/12Markov random fields II
HW5 and project progress report due (on Saturday)
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25Tue 11/17Learning Theory I
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26Thu 11/19Learning Theory II
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Thanksgiving Break
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Thanksgiving Break
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27Tue 12/1Project presentations I
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28Thu 12/3Project presentations II
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Tue 12/8Final Wrap UpProject writeup due
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ExamMon 12/14Take home final
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