| Date |
Lecture |
Slides |
Useful links |
|
|
|
|
|
| August 30 Monday |
Intro to ML concepts |
Intro.pdf, Lecture1_inked |
Murphy: Sec 1.1-1.3 |
| September 1 Wednesday |
Bayes classifier, Decision Boundary |
BayesClassifier_DecisionBoundary.pdf, Lecture2_inked.pdf |
Bishop: Sec 1.5 |
| September 6 Monday |
Labor Day -- No class |
|
|
| September 8 Wednesday |
MLE |
MLE_MAP.pdf, Lecture3_inked.pdf |
Bishop: Sec 2.1-2.3.6, Mitchell_Ch |
| September 13 Monday |
MAP, Naive Bayes |
NaiveBayes.pdf, Lecture4_inked.pdf |
Mitchell_Ch (Secs 1-2) |
| September 15 Wednesday |
Logistic Regression |
LogisticRegression.pdf, Lecture5_inked.pdf |
Mitchell_Ch (Secs 3-5), On Discriminative and Generative Classifiers, Ng and Jordan, NIPS, 2001 (pdf) |
| September 20 Monday |
Linear regression |
LinearReg.pdf, Lecture6_inked.pdf |
Murphy: Sec 7.1-7.3 |
| September 22 Wednesday |
Regularization, Nonlinear regression |
Regularized_LinReg.pdf, Lecture7_inked.pdf |
Murphy: Sec 7.5-7.6 |
| September 27 Monday |
Neural networks |
NeuralNets.pdf, Lecture8_inked.pdf |
Goodfellow et al: Ch 6, Demo |
| September 29 Wednesday |
Neural networks |
Lecture9_inked.pdf |
Goodfellow et al: Ch 6 |
| October 4 Monday |
Deep Convolutional Neural Networks |
CNN.pdf, Lecture10_inked.pdf |
Bishop: Sec 2.5, Goodfellow et al: Ch 9 |
| October 6 Wednesday |
Decision Trees |
DecisionTrees, Lecture11_inked.pdf |
Mitchell: Ch 3 |
| October 11 Monday |
Nonparametric methods - density estimation, kernel regression, Nearest neighbors |
nonparametric.pdf, Lecture12_inked.pdf |
Bishop: Sec 2.5, Notes Eduardo, Murphy: Sec 1.4 |
| October 13 Wednesday |
Mid-term review |
Lecture13_inked.pdf |
|
| October 18 Monday |
Midterm Quiz (in-class) |
|
|
| October 20 Wednesday |
Support Vector Machines (hard, soft) |
SVM.pdf, Lecture14_inked.pdf |
Bishop: Sec 7.1.1-7.1.3, Sec 4.1.1, 4.1.2, Appendix E |
| October 25 Monday |
Guest lecture-data issues |
|
|
| October 27 Wednesday |
Support Vector Machines (dual) |
SVM_dual.pdf, Lecture15_inked.pdf |
Bishop: Sec 7.1.1-7.1.3, Sec 4.1.1, 4.1.2, Appendix E |
| November 1 Monday |
Kernelized SVM, Logistic and Linear Regression |
Dual_Kernels, Lecture16_inked.pdf |
Bishop: Sec 6.1, 6.2, SVMdemo,
Slides 52-56 KRR Dual derivation, Welling's KRR Notes.pdf |
| November 3 Wednesday |
Boosting |
Boosting.pdf, Lecture17_inked.pdf |
Bishop: Sec 14.3
Schapire: Boosting Tutorial, Video |
| November 8 Monday |
Model selection, cross-validation |
ModelSel.pdf, Lecture18_inked.pdf |
Bishop: Sec 1.3, 3.2 |
| November 10 Wednesday |
Dimensionality Reduction (PCA) |
Dim_Red_PCA.pdf, Lecture19_inked.pdf |
Bishop Ch. 12 through 12.1 |
| November 15 Monday |
Clustering, Mixture models |
clustering.pdf, Lecture20_inked.pdf |
Bishop: Sec 9.1,9.2 |
| November 17 Wednesday |
Expectation-Maximization |
EM_GMM.pdf, Lecture21_inked.pdf |
Bishop: Sec 9.1,9.2 |
| November 22 Monday |
Learning Theory (PAC bounds) |
theory.pdf, Lecture22_inked.pdf |
Mitchell: Ch 7, Murphy: Sec 6.5.4 |
| November 24 Wednesday |
Thanksgiving -- No class |
|
|
| November 29 Monday |
Final review |
|
|
| December 1 Wednesday |
Final Quiz (in-class) |
|
|
