Date |
Topic |
Slides |
Useful links |
HWs |
HWs Topics |
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Jan 18 Wednesday |
Intro to ML concepts |
Intro, Lecture1_inked.pdf |
Murphy: Sec 1.1-1.3 |
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Jan 20 Friday |
Probability |
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Jan 23 Monday |
Naive Bayes |
NaiveBayes, Lecture2_inked.pdf |
Bishop: Sec 1.5, Mitchell Ch3, Secs 1,2 |
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Jan 25 Wednesday |
MLE, MAP |
MLE_MAP, Lecture3_inked.pdf |
Bishop: Sec 2.1-2.3.6, Mitchell Ch2 |
HW1 Out |
MLE/MAP, Naive Bayes, Logistic Regression |
Jan 27 Friday |
Convexity and Optimization |
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|
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Jan 30 Monday |
Logistic Regression |
LogisticRegression, Lecture4_inked.pdf |
Mitchell_Ch (Secs 3-5), On Discriminative and Generative Classifiers, Ng and Jordan, NIPS, 2001 (pdf) |
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|
Feb 1 Wednesday |
SVM I |
Support vector machines, Lecture5_inked.pdf |
Bishop: Sec 7.1.1-7.1.3, Sec 4.1.1, 4.1.2, Appendix E |
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Feb 3 Friday |
Duality |
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Feb 6 Monday |
SVMs II |
SVM_Dual_formulation, Lecture6_inked.pdf
|
Bishop: Sec 7.1.1-7.1.3, Sec 4.1.1, 4.1.2, Appendix E |
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|
Feb 8 Wednesday |
Kernels |
Kernel_trick, Lecture7_inked.pdf |
Bishop: Sec 6.1, 6.2, SVMdemo,
Kernelized logistic regression Dual derivation |
HW1 Due, HW2 Out |
SVM, Deep Learning |
Feb 10 Friday |
Optimization for ML |
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|
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Feb 13 Monday |
Neural Nets |
Neural_Nets |
Goodfellow et al: Ch 6, Demo |
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Feb 15 Wednesday |
Deep Neural Nets |
CNN, Lecture9_inked.pdf |
Goodfellow et al: Ch 8,9 |
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Feb 17 Friday |
Deep Learning |
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Feb 20 Monday |
Decision Trees |
Decision_trees, Lecture10_inked.pdf |
Mitchell: Ch3 |
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Feb 22 Wednesday |
No Class - CANCELED |
|
|
HW2 Due |
|
Feb 24 Friday |
Review |
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Feb 27 Monday |
k-NN classifier |
k-NNclassifier, Lecture11_inked.pdf
| Bishop: Sec 2.5, Notes Eduardo, Murphy: Sec 1.4 |
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Mar 1 Wednesday |
In-class Midterm Quiz |
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Mar 3 Friday |
No Recitation |
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Mar 6 Monday |
No class: Spring Break |
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Mar 8 Wednesday |
No class: Spring Break |
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Mar 10 Friday |
No Recitation: Spring Break |
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Mar 13 Monday |
Boosting |
Boosting, Lecture12_inked.pdf |
Bishop: Sec 14.3 Schapire: Boosting Tutorial, Video |
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Mar 15 Wednesday |
Model selection |
Model_selection, Lecture13_inked.pdf |
Bishop: Sec 1.3, 3.2 |
Depth exercise proposal Due, HW3 Out |
Decision Trees, Boosting, Nonparametrics, Regression |
Mar 17 Friday |
Linear Algebra |
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Mar 20 Monday |
Linear Regression |
Linear Regression, Lecture14_inked.pdf |
Murphy: Sec 7.1-7.3 |
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Mar 22 Wednesday |
Regularized, Kernel, and GP Regression |
Regularized Linear Regression, Lecture15_inked.pdf |
Murphy: Sec 7.5-7.6, Kernelized ridge regression |
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Mar 24 Friday |
Regression |
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Mar 27 Monday |
FATE - Guest Lecture |
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Mar 29 Wednesday |
Learning Theory |
Learning Theory, Lecture16_inked.pdf |
Mitchell: Ch 7, Murphy: Sec 6.5.4 |
HW3 Due, HW4 Out |
RL, Theory, Depth exercise |
Mar 31 Friday |
Theory |
|
|
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Apr 3 Monday |
Reinforcement learning I |
MDP_value_policy_iteration, Lecture17_inked.pdf |
RL Survey paper |
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Apr 5 Wednesday |
Reinforcement Learning II |
Qlearning, Lecture18_inked.pdf |
RL Survey paper |
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Apr 7 Friday |
RL |
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Apr 10 Monday |
Hidden Markov Models |
HMM
| Bishop: Ch 13, HMM and EM tutorial |
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Apr 12 Wednesday |
Graphical Models I |
Graphical Models I, Lecture19_inked.pdf |
Bishop: Ch 8
Graphical Models tutorial by M. Jordan
Intro to Graphical Models by K. Murphy |
HW4 Due, HW5 Out |
HMM, Graphical Models, PCA |
Apr 14 Friday |
No Recitation: Carnival |
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|
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Apr 17 Monday |
Graphical models II |
Graphical Models II, Lecture20_inked.pdf |
Bishop: Ch 8
Graphical Models tutorial by M. Jordan
Intro to Graphical Models by K. Murphy |
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|
Apr 19 Wednesday |
Dimensionality Reduction, PCA |
PCA, Lecture21_inked.pdf |
Bishop Ch. 12 through 12.1 |
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Apr 21 Friday |
Graphical Models |
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|
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Apr 24 Monday |
Clustering |
Clustering, Lecture22_inked |
Bishop: Sec 9.1,9.2 |
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Apr 26 Wednesday |
GMM, Hierarchical clustering |
Lecture23_inked |
Bishop: Sec 9.1,9.2 |
HW5 Due |
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Apr 28 Friday |
Review |
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May 1 - May 8 |
Final Quiz - May 4th, 9:30 AM - 11:30 AM, BH A51 |
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