Introduction to Machine Learning

10-701, Spring 2021

Carnegie Mellon University

Geoff Gordon, Aarti Singh


Home Teaching Staff Lecture Schedule Recitations Homeworks

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

Date Lecture Slides Useful links HWs
Feb 1 Monday Intro to ML concepts Intro, Lecture1_inked.pdf Murphy: Sec 1.1-1.3
Feb 3 Wednesday Naive Bayes, MLE, MAP MLE_MAP_NaiveBayes, Lecture2_inked.pdf Bishop: Sec 2.1-2.3.6, 1.5, Mitchell Ch2
Feb 8 Monday Naive Bayes, MLE, MAP Lecture3_inked.pdf BayesOptimalityProof HW1 out
Feb 10 Wednesday Nonparametric methods Lecture4_inked.pdf, Nonparametrics
Feb 15 Monday Information theory InformationTheory
Feb 17 Wednesday Decision Trees, Linear regression DecisionTrees, LinearRegression Murphy: Sec 7.1-7.3 HW1 due
Feb 22 Monday Regularized linear regression, Logistic regression Lecture7a_inked.pdf, LogisticRegression, Lecture7b_inked.pdf Mitchell_Ch (Secs 3-5), On Discriminative and Generative Classifiers, Ng and Jordan, NIPS, 2001 (pdf)
Feb 24 Wednesday Optimization for ML Optimization
Mar 1 Monday Deep learning I Deep Learning
Mar 3 Wednesday Deep learning II Representation & Sequence learning
Mar 8 Monday Deep learning III Convolutional NNs, CNN_inked.pdf Goodfellow et al: Ch9
Mar 10 Wednesday Best practices, Model selection ModelSel_BestPrac, ModelSel_inked.pdf
Mar 15 Monday Function spaces, Duality duality.pdf
Mar 17 Wednesday Duality in deep learning duality-gans.pdf
Mar 22 Monday SVMs I SVM, SVM_inked.pdf Bishop: Sec 7.1.1-7.1.3, Sec 4.1.1, 4.1.2, Appendix E
Mar 24 Wednesday SVMs II SVMduality&kernels, SVMduality_kernels_inked.pdf Bishop: Sec 6.1, 6.2, SVMdemo
Mar 29 Monday FATE issues
Mar 31 Wednesday Reinforcement learning I Policy Gradient
Apr 5 Monday No class: Break day
Apr 7 Wednesday Reinforcement learning II
Apr 12 Monday Reinforcement learning III
Apr 14 Wednesday Learning theory I PAC_theory, PAC_inked Mitchell: Ch 7, Murphy: Sec 6.5.4
Apr 19 Monday Learning theory II RademacherComplexity, Rademacher_inked Rademacher_Kernel
Apr 21 Wednesday Hidden Markov Models HMM, HMM_inked Bishop: Ch 13, HMM and EM tutorial
Apr 26 Monday Graphical models I
Apr 28 Wednesday Graphical models II
May 3 Monday Dimensionality reduction, PCA DimReduction, DimRed_inked Bishop: Ch12, KernelPCA
May 5 Wednesday Clustering, Review Clustering, Clustering_inked Bishop: Sec 9.1,9.2