cs536 – Machine Learning


Vladimir Pavlovic

Note: If you do not satisfy prerequisites for this course and want to take it with me, please read my announcements regarding SPN/Prerequisite overrides.  I will post instructions ahead of each semester.

Spring 2018 Instructions are here.

Course Description

An in-depth study of supervised methods for machine learning, to impart an understanding of the major topics in this area, the capabilities and limitations of existing methods, and research topics in this field.


Inductive learning, including decision-tree, Bayesian methods, computational learning theory, instance-based learning, explanation-based learning, reinforcement learning, nearest neighbor methods, PAC-learning, kernels methods, graphical models, regression modeling, deep models.

Expected Work

Regular readings; mini-projects; in-class presentations; midterm and a final course project.

Course Policies and Procedures

Important, perhaps boring details. But please read them carefully.


Topic #TitleText
1Introduction to Supervised Learning
FML Ch 1
PRML Ch 1.1 - 1.4
MLPP Ch 1.1 - 1.3
DL Ch 5.1
ML Ch 1
2Overview of linear algebra and probabilityPRML Ch 2
3Overview of optimization; Gradient Descent; Second Order MethodsDL Ch 4
4Linear Regression; Overfitting and Ridge Regression; Bias-Variance Decomposition; Risk MinimizationPRML Ch 3.1 - 3.2
MLPP Ch 6.1 - 6.5, 7.1 - 7.5
ESL Ch 3 - 4
5Decision Theory; Generative Classification Models, Linear Discriminant Analysis; Na•ve BayesPRML Ch 1.5, Ch. 4.1 - 4.2
PPML Ch 3, Ch 4.1 - 4.2
6Design and Analysis of Machine Learning Experiments; Model AssesementI2ML Ch 19
MLPP Ch. 7
7Discriminative Classification Models; Logistic Regression; Bias-Variance Decomposition in ClassificationPRML Ch. 4.3
MLPP Ch. 8.1 - 8.3
8Bayesian Learning; Bayesian Linear Regression & Bayesian Logistic Regression; Generalized Linear ModelsMLPP Ch. 5, 7.6, 8.4, 9
PRML Ch. 3.3 - 3.4, 4.4 - 4.5
9Sparse Models and Feature SelectionMLPP Ch. 13
10Kernel Models; RBF Networks; Kernel TrickPRML Ch. 6.1 - 6.3
MLPP Ch. 14.1 - 14.2, 14.4
11Support Vector Machines; Relevance Vector MachinePRML Ch. 7
MLPP Ch. 14.5 - 14.7, 14.3
12Gaussian Process ModelsPRML Ch. 6.4
MLPP Ch. 15
13Adaptive Basis Models; Decision and Regression TreesMLPP Ch. 16.1-16.3
PRML Ch. 14.4
ESL Ch. 9
14Ensemble Models; Boosting; Stacking; Mixtures of ModelsMLPP Ch. 16.4, 16.6
PRML Ch. 14.1 - 14.3
ESL Ch. 10
15Neural Networks; Feedforward Networks; Gradient Learning; BackpropagationPRML Ch. 5
MLPP Ch. 16.5
DL Ch. 6
16Deep Generative Models; Deep Neural NetworksMLPP Ch. 28.1 - 28.3
17Regularization and Optimization in Deep ModelsDL Ch. 7 - 8
18Convolutional NetworkDL Ch. 9
19Structured Prediction; Conditional Random Fields; Structured SVMs; Prediction on GraphsMLPP Ch. 19
20Sequential Deep Models; Recurrent Neural NetworksDL Ch. 10
21Reinforcement Learning; Deep Reinforcement Learninghttps://web.mst.edu/~gosavia/tutorial.pdf


AbbreviationTextbook TitleAuthorPublisherYear
PRMLPattern Recognition and Machine LearningChristopher C. BishopSpringer2006
MLPPMachine Learning: A Probabilistic PerspectiveKevin P. MurphyMIT Press2012
CVMLIComputer vision: models, learning and inferencePrince, Simon J DCambridge University Press2012
DLDeep LearningGoodfellow, Ian and Bengio, Yoshua and Courville, AaronMIT Press2016
FMLFoundations of Machine LearningMohri, Mehryar and Rostamizadeh, Afshin and Talwalkar, AmeetMIT Press2012
DHSPattern Classification, 2nd edDuda, Richard O. and Hart, Peter E. and Stork, David G.Wiley Interscience2004
MLMachine LearningMitchell, TomMcGraw Hill1997
I2MLIntroduction to Machine Learning, 2nd edAlpaydin, EthemMIT Press2012
MLAPMachine Learning: An Algorithmic perspectiveMarsland, StephenCRC press2009
PTPRA Probabilistic Theory of Pattern RecognitionDevroye, Luc and Gyorfi, Laszlo and Lugosi, GaborSpringer1997
ESLThe elements of statistical learning: Data mining, inference, and predictionFriedman, J and Hastie, T and Tibshirani, RSpringer2009
NAPRNetlab: Algorithms for Pattern RecognitionNabney, IanSpringer2002
DMPMLTTData Mining: Practical Machine Learning Tools and TechniquesWitten, Ian H and Frank, EibeMorgan Kaufmann2005
LAALinear Algebra and Its ApplicationsStrang, GilbertElsevier Science2014
MCMatrix computations, 4th edGolub, Gene H and Van Loan, Charles FJHU Press2013
COConvex OptimizationBoyd, Steven P and Vandenberghe, LievenCambridge University Press2004
ILCOIntroductory lectures on convex optimization: a basic course_Nestorov, YuriiSpringer2004
GPMLGaussian Processes for Machine LearningRasmussen, Carl Edward and Williams, Christopher K. I. MIT Press2006
ITILAInformation Theory, Inference, and Learning AlgorithmsMacKay, DavidCambridge University Press2003
HOMLHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent SystemsGéron, AurélienO’Reilly Media, Inc.2019
PMLIProbabilistic Machine Learning: An IntroductionMurphy, Kevin P.MIT Press2022
PMLAProbabilistic Machine Learning: Advanced TopicsMurphy, Kevin P.MIT Press2022


We will use Python and MATLAB extensively!