Introduction to Machine Learning

10-315, Spring 2022

Carnegie Mellon University

Aarti Singh, Henry Chai

Home Teaching Staff Lecture Schedule Recitations Homeworks

Note: this is a tentative lecture schedule that is subject to change.

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