| Date |
Lecture topic |
Slides |
Useful links |
|
|
|
|
|
| January 19 Wednesday |
Intro to ML concepts |
Intro.pdf, Lecture1_inked |
Murphy: Sec 1.1-1.3 |
| January 24 Monday |
Bayes classifier, Decision Boundary |
BayesClassifier_DecisionBoundary.pdf, Lecture2_inked.pdf |
Bishop: Sec 1.5 |
| January 26 Wednesday |
MLE |
MLE.pdf, Lecture3_inked.pdf |
Bishop: Sec 2.1-2.3.6, Mitchell_Ch |
| January 31 Monday |
MAP, Naive Bayes |
MAP.pdf, NaiveBayes.pdf, BernoulliMLE_derivation |
Mitchell_Ch (Secs 1-2) |
| February 2 Wednesday |
Logistic Regression |
LogisticRegression.pdf, Lecture5a_inked.pdf, Lecture5b_inked.pdf |
Mitchell_Ch (Secs 3-5), On Discriminative and Generative Classifiers, Ng and Jordan, NIPS, 2001 (pdf) |
| February 7 Monday |
Linear regression |
LinearReg.pdf, Lecture6a_inked.pdf, Lecture6b_inked.pdf |
Murphy: Sec 7.1-7.3 |
| February 9 Wednesday |
Regularization, Nonlinear regression |
Regularization.pdf, Lecture7_inked.pdf |
Murphy: Sec 7.5-7.6 |
| February 14 Monday |
Neural networks |
NeuralNets.pdf, Lecture8_inked.pdf |
Goodfellow et al: Ch 6, Demo |
| February 16 Wednesday |
Neural networks |
NN_CNN.pdf |
Goodfellow et al: Ch 6 |
| February 21 Monday |
Deep Convolutional Neural Networks |
CNNcontd.pdf, Lecture10_inked.pdf |
Bishop: Sec 2.5, Goodfellow et al: Ch 9 |
| February 23 Wednesday |
Nonparametric methods - density estimation, kernel regression, nearest neighbors |
nonparametric.pdf, Lecture11_inked.pdf |
Bishop: Sec 2.5, Notes Eduardo, Murphy: Sec 1.4 |
| February 28 Monday |
Decision Trees |
DecisionTrees, Lecture12_inked.pdf |
Mitchell: Ch 3 |
| March 2 Wednesday |
Boosting |
Boosting.pdf, Lecture13_inked.pdf |
Bishop: Sec 14.3
Schapire: Boosting Tutorial, Video |
| March 7 Monday |
Spring Break -- No Class |
|
|
| March 9 Wednesday |
Spring Break -- No Class |
|
|
| March 14 Monday |
Mid-term review |
Lecture14_inked.pdf, MidtermReview.pdf |
|
| March 16 Wednesday |
Midterm Quiz (in-class) |
|
|
| March 21 Monday |
Support Vector Machines (hard, soft) |
SVMs.pdf, Lecture15_inked.pdf |
Bishop: Sec 7.1.1-7.1.3, Sec 4.1.1, 4.1.2, Appendix E |
| March 23 Wednesday |
Support Vector Machines (dual) |
SVM_dual.pdf, Lecture16_inked.pdf |
Bishop: Sec 7.1.1-7.1.3, Sec 4.1.1, 4.1.2, Appendix E |
| March 28 Monday |
Kernelized SVM, Logistic and Linear Regression |
Dual_Kernels, Lecture17_inked.pdf |
Bishop: Sec 6.1, 6.2, SVMdemo,
Slides 52-56 KRR Dual derivation, Welling's KRR Notes.pdf |
| March 30 Wednesday |
Model selection, cross-validation |
ModelSel.pdf, Lecture18_inked.pdf |
Bishop: Sec 1.3, 3.2 |
| April 4 Monday |
Dimensionality Reduction (PCA) |
Dim_Red_PCA.pdf, Lecture19_inked.pdf |
Bishop Ch. 12 through 12.1 |
| April 6 Wednesday |
Clustering, Mixture models |
Lecture20a_inked.pdf,
Clustering.pdf, Lecture20b_inked.pdf |
Bishop: Sec 9.1,9.2 |
| April 11 Monday |
Expectation-Maximization |
EM_GMM.pdf, Lecture21_inked.pdf |
Bishop: Sec 9.1,9.2 |
| April 13 Wednesday |
End-to-end Machine Learning Pipelines |
ML_pipelines.pdf |
|
| April 18 Monday |
Gaussian Processes |
GPs.pdf, Lecture23_inked.pdf |
|
| April 20 Wednesday |
Learning Theory (PAC bounds) |
Theory.pdf
| Mitchell: Ch 7, Murphy: Sec 6.5.4 |
| April 25 Monday |
Fairness and Machine Learning |
Fairness.pdf |
|
| April 27 Wednesday |
Final review |
|
|
