Introduction to Machine Learning

10-701, Spring 2023

Carnegie Mellon University

Aarti Singh


Home Teaching Staff Lecture Schedule

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

Date Topic Slides Useful links HWs HWs Topics
Jan 18 Wednesday Intro to ML concepts Intro, Lecture1_inked.pdf Murphy: Sec 1.1-1.3
Jan 20 Friday Probability
Jan 23 Monday Naive Bayes NaiveBayes, Lecture2_inked.pdf Bishop: Sec 1.5, Mitchell Ch3, Secs 1,2
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
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)
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
Feb 3 Friday Duality
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
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
Feb 13 Monday Neural Nets Neural_Nets Goodfellow et al: Ch 6, Demo
Feb 15 Wednesday Deep Neural Nets CNN, Lecture9_inked.pdf Goodfellow et al: Ch 8,9
Feb 17 Friday Deep Learning
Feb 20 Monday Decision Trees Decision_trees, Lecture10_inked.pdf Mitchell: Ch3
Feb 22 Wednesday No Class - CANCELED HW2 Due
Feb 24 Friday Review
Feb 27 Monday k-NN classifier k-NNclassifier, Lecture11_inked.pdf Bishop: Sec 2.5, Notes Eduardo, Murphy: Sec 1.4
Mar 1 Wednesday In-class Midterm Quiz
Mar 3 Friday No Recitation
Mar 6 Monday No class: Spring Break
Mar 8 Wednesday No class: Spring Break
Mar 10 Friday No Recitation: Spring Break
Mar 13 Monday Boosting Boosting, Lecture12_inked.pdf Bishop: Sec 14.3
Schapire: Boosting Tutorial, Video
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
Mar 20 Monday Linear Regression Linear Regression, Lecture14_inked.pdf Murphy: Sec 7.1-7.3
Mar 22 Wednesday Regularized, Kernel, and GP Regression Regularized Linear Regression, Lecture15_inked.pdf Murphy: Sec 7.5-7.6, Kernelized ridge regression
Mar 24 Friday Regression
Mar 27 Monday FATE - Guest Lecture
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
Apr 3 Monday Reinforcement learning I MDP_value_policy_iteration, Lecture17_inked.pdf RL Survey paper
Apr 5 Wednesday Reinforcement Learning II Qlearning, Lecture18_inked.pdf RL Survey paper
Apr 7 Friday RL
Apr 10 Monday Hidden Markov Models HMM Bishop: Ch 13, HMM and EM tutorial
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
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
Apr 19 Wednesday Dimensionality Reduction, PCA PCA, Lecture21_inked.pdf Bishop Ch. 12 through 12.1
Apr 21 Friday Graphical Models
Apr 24 Monday Clustering Clustering, Lecture22_inked Bishop: Sec 9.1,9.2
Apr 26 Wednesday GMM, Hierarchical clustering Lecture23_inked Bishop: Sec 9.1,9.2 HW5 Due
Apr 28 Friday Review
May 1 - May 8 Final Quiz - May 4th, 9:30 AM - 11:30 AM, BH A51