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Course Schedule / Reading / Assignments

Assignment Handed Out Due Date Percentage
A1: Statistical Foundations Tue 7 Sep Tue 21 Sep 7%
A2: Decision Trees Thu 23 Sep Tue 5 Oct 7%
A3: k-means and Mixture Modelling Thu 7 Oct Thu 14 Oct
(but no penalty before Tue 19 Oct)		 7%
A4: Neural Networks Tue 26 Oct Thu 4 Nov 7%
P1: Classifier Project Thu 4 Nov Tue 23 Nov 10%
A5: Graphical Models Tue 9 Nov Thu 18 Nov 7%
A6: Time Series and Reinforcement Learning Tue 23 Nov Thu 2 Dec 7%
P2: Independent Project Tue 23 Nov Fri 17 Dec 10%
Wk Tuesday 5-6:20 Thursday 5-6:20
1 Aug 31
Introduction and Overview of course
Mi 1		 Sep 2
Probability, Entropy, and Inf.Th. (1)
Mi 5.1-3, Ma 2.1-5
2 Sep 7 (a1 out)
Probability, Entropy, and Inf.Th. (2)		 Sep 9
Probability, Entropy, and Inf.Th. (3)
3 Sep 14
Decision Trees (1)		 Sep 16
no lecture, Rosh Hashanah
4 Sep 21 (a1 due)
Decision Trees (2)		 Sep 23 (a2 out)
Latent Variable Models for Clustering & DimReduct (1)
5 Sep 28
Latent Variable Models for Clustering & DimReduct (2)		 Sep 30
Latent Variable Models for Clustering & DimReduct (3)
6 Oct 5 (a2 due)
Neural Networks (1)		 Oct 7 (a3 out)
Neural Networks (2)
7 Oct 12
Neural Networks (3)		 Oct 14
review of topics covered so far
8 Oct 19 (a3 due)
Midterm Exam		 Oct 21
Neural Networks (4)
9 Oct 26 (a4 out)
Learning with hidden variables (1)		 Oct 28
Learning with hidden variables (2)
10 Nov 2
Graphical Models (1)		 Nov 4 (a4 due, p1 out)
Graphical Models (2)
11 Nov 9
Time Series Models (1)		 Nov 11 (a5 out)
Time Series Models (2)
12 Nov 16
Reinforcement Learning (1)		 Nov 18
Reinforcement Learning (2)
13 Nov 23 (a5 due, p2 out)
Support Vector Machines (1)		 Nov 25 : (p1 due)
no lecture, Fall Recess
14 Nov 30 (a6 out)
Support Vector Machines (2)		 Dec 2
Topic to be decided
15 Dec 7
catch up lecture		 Dec 9 (a6 due)
review of course syllabus
16 Dec 13 (Monday) Final Exam 1145--1445 FILMOR 355 Dec 17 (Friday) (p2 due)