[Spring 2014 –16:198:536] Machine Learning
Schedule / Syllabus (Subject to Change)
Lecture # |
Day |
Date |
Topic |
Readings |
Notes |
1 |
Tue |
Jan 21 |
Introduction |
á
Murphy Ch. 1 á
Bishop Ch. 1 á
http://www.cs.cmu.edu/~tom/pubs/MachineLearning.pdf
Optional á
Mitchell Ch. 1 |
|
2 |
Thu |
Jan 23 |
Linear
Regression & Bias-variance
Tradeoff |
á
Murphy Ch. 7.1-7.5 á
Bishop Ch. 3.1-3.2 Optional á
HTF Ch. 3 & 4 |
|
3 |
Tue |
Jan 28 |
Overfitting,
Regularization, Sparsity,
& Evaluation |
á
Murphy Ch. 7.1-7.5 á
Bishop Ch. 3.1-3.2 á
http://www.autonlab.org/tutorials/overfit10.pdf á
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.37.3325 Optional á
HTF Ch. 3 & 4 |
|
4 |
Thu |
Jan 30 |
Logistic
Regression & Na•ve Bayes |
á
Murphy Ch. 5 & 8 á
Bishop Ch. 4 |
|
5 |
Tue |
Feb 4 |
Gaussian
Na•ve Bayes & Generative
vs. Discriminative Classifiers |
á
http://www.cs.cmu.edu/~tom/mlbook/NBayesLogReg.pdf
Optional: á
http://ai.stanford.edu/~ang/papers/nips01-discriminativegenerative.pdf
á
http://select.cs.cmu.edu/class/10701-F09/readings/bag-of-words.pdf |
HW #1 out. |
6 |
Thu |
Feb 6 |
Decision
Trees & IBL |
á
http://eliassi.org/Nilsson-ch6-ml.pdf á
Murphy Ch. 16 á
HTF 13.3-13.5 Optional:
á
Mitchell Ch. 3 & 8 |
|
7 |
Tue |
Feb 11 |
Ensemble
Methods |
á
http://eliassi.org/boosting-schapire.pdf á
http://www.springerlink.com/content/u0p06167n6173512/
á
Murphy Ch. 16 á
Bishop Ch. 14 Optional á
HTF Ch. 10, 15, 16 |
|
8 |
Thu |
Feb 13 |
No class (snow day) |
|
HW #1 due. |
9 |
Tue |
Feb 18 |
Ensemble
Methods &
Perceptron |
á
https://www.cs.princeton.edu/~schapire/papers/explaining-adaboost.pdf
|
|
10 |
Thu |
Feb 20 |
Artificial
Neural Networks & Deep Learning |
á
Bishop Ch. 4.1.7 & 5 á
Murphy Ch. 28 Optional á
Mitchell 4 á
HTF 11 |
HW #2 out. |
11 |
Tue |
Feb 25 |
SVM &
Kernels |
á
http://research.microsoft.com/en-us/um/people/cburges/papers/SVMTutorial.pdf á
http://select.cs.cmu.edu/class/10701-F09/readings/hearst98.pdf
á
http://www.isn.ucsd.edu/pubs/nips00_inc.pdf
á
Bishop Ch. 6 & 7 á
Murphy Ch. 14 Optional á
HTF 6, 12. |
|
12 |
Thu |
Feb 27 |
Sample
Complexity, PAC, & VC
Dimension |
á
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=788640 á
http://eliassi.org/COLTSurveyArticle.pdf á
http://jmlr.org/papers/volume6/langford05a/langford05a.pdf á
http://www.cs.princeton.edu/courses/archive/spr08/cos511/scribe_notes/0227.pdf á
Bishop Ch. 7.1.5 á
Murphy Ch. 6.5 & 6.6 Optional: á
http://www.cs.cmu.edu/~avrim/Papers/survey.pdf
á
Mitchell 7 á
HTF 7 |
HW #1 graded. |
13 |
Tue |
Mar 4 |
Kernel
Learning for Structured Prediction (Guest
Lecturer: |
á
Reading material in the Resources folder on Sakai |
HW #2 due. |
14 |
Thu |
Mar 6 |
Bayesian
Networks |
á
Murphy Ch. 10 á
Bishop Ch. 8 |
|
15 |
Tue |
Mar 11 |
Midterm Exam (TA will
proctor) |
|
|
16 |
Thu |
Mar 13 |
In-class Proposal
Pitches |
HW #2 graded. |
|
17 |
Tue |
Mar 18 |
Spring Break |
|
|
18 |
Thu |
Mar 20 |
Spring Break |
|
|
19 |
Tue |
Mar 25 |
Bayesian
Networks & Graphical Models |
á
http://www.cs.cmu.edu/~aarti/Class/10701/readings/graphical_model_Jordan.pdf á
http://www.cs.cmu.edu/~aarti/Class/10701/readings/intro_gm.pdf |
Proposal pitches
graded. |
20 |
Thu |
Mar 27 |
Clustering |
á
Murphy Ch. 25 á
Bishop Ch. 9 Optional: á
HTF 14 |
Midterm graded. |
21 |
Tue |
Apr 1 |
Gaussian
Mixture Models & Expectation Maximization |
á
Murphy Ch. 11 á
Bishop Ch. 9 |
|
22 |
Thu |
Apr 3 |
Hidden Markov
Models |
á
Murphy Ch. 17 á
Bishop Ch. 13 á
http://www.cs.cmu.edu/~aarti/Class/10701/readings/gentle_tut_HMM.pdf
|
HW#3 out. |
23 |
Tue |
Apr 8 |
Latent
Variable Models |
á
Murphy Ch. 27 |
|
24 |
Thu |
Apr 10 |
Learning on
Graphs |
á
http://eliassi.org/papers/ai-mag-tr08.pdf |
|
25 |
Tue |
Apr 15 |
Dimensionality
Reduction |
á
http://www.snl.salk.edu/~shlens/pca.pdf
á
http://www.cs.cmu.edu/~tom/10701_sp11/slides/CCA_tutorial.pdf
á
Bishop Ch. 12 Optional: |
HW #3 due. |
26 |
Thu |
Apr 17 |
1. What Can
20,000 Models Teach Us? 2. Reliable
Differential Dependency Network Analysis (Guest Lecturer: |
á
http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml04.icdm06long.pdf
á
http://www.niculescu-mizil.org/papers/calibration.icml05.crc.rev3.pdf
á
http://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf
|
|
27 |
Tue |
Apr 22 |
Reinforcement
Learning |
á
http://www.cs.cmu.edu/~tom/10701_sp11/slides/Kaelbling.pdf á
http://www.research.rutgers.edu/~lihong/pub/Li11Knows.pdf
Optional: á
Mitchell Ch. 13 |
|
28 |
Thu |
Apr 24 |
In-class Final Exam (TA will
proctor) |
|
|
29 |
Tue |
Apr 29 |
In-class Project
Presentations |
|
HW #3 graded. |
30 |
Thu |
May 1 |
In-class Project
Presentations |
Last day of class. |
|
-- |
Tue |
May 6 |
-- |
|
Project presentations
graded. |
-- |
Tue |
May 13 |
-- |
|
Final exam graded. Project reports
due. |
-- |
Thu |
May 15 |
-- |
|
Project reports graded. Final grades released. |