We will use the following textbooks for this course:
[HTF] The elements of statistical learning: data mining, inference and prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman. Springer. 2001. Q325.75.F75 2001 c. 1. Available at http://statweb.stanford.edu/~tibs/ElemStatLearn/.
[BIS] Pattern recognition and machine learning. Christopher M. Bishop. 2009. Q327.B52 2009 c. 1
Other useful references are:
[MRT] Foundations of machine learning. Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. 2012. Q325.5 .M64 2012 c. 1
[SSBD] Understanding Machine Learning: From Theory to Algorithms. Shai ShalevShwartz and Shai BenDavid. 2014. Q325.5 .S475 2014 c. 1. Available at http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understandingmachinelearningtheoryalgorithms.pdf.
[MUR] Machine learning: a probabilistic perspective. Kevin P. Murphy. 2012. Q325.5 .M87 2012 c. 1
[TM] Machine learning. Tom M. Mitchell. 1997. Q325.5.M58 1997 c. 1
Date 
Topics 
Readings 

Introduction 

T 
Introduction to Machine Learning Define learning, why/when do we need machine learning, discuss different types of machine learning, recent success, cool applications 
MUR Chapter 1 PDF, 

Th 
Course overview, formal introduction Supervised learning: task, performance, evaluation; classification, regression, loss function, risk 
MRT Chapter 1 

F 
Recitation: Probability and Statistics Events, random variables, probabilities, pdf, pmf, cdf, mean, mode, median, variance, multivariate distributions, marginals, conditionals, Bayes theorem, independence 
Review slides


T 
Foundations Bayes optimal rule, Bayes risk, empirical risk minimization (ERM), generalization error, supervised learning: classification/regression, rote learning, lazy learning, model fitting 
SSBD Chapter 2, HTF Section 2.12.3, MUR Sections 1.11.2 

F 
Recitation: Linear Algebra Vector spaces, norms, metric spaces, inner product spaces, CauchySchwarz, Orthonormal bases 

F 
Recitation: MLE, MAP, Intro Python
Parametric distributions, parameter estimation (MLE), MAP; introduction to Python, Jupyter notebook, numpy 
BIS Chapter 2,


T 
Linear Regression Linear functions, loss function, empirical risk minimization, least squares solution, generalization, error decomposition 
HTF Sections 2.3, 3.2,
BIS Section 3.1 

Th 
Error analysis, statistical view Bayes optimal predictor, statistical view, Gaussian model, Maximum Likelihood Estimation (MLE), Polynomial regression, general additive regression, overfitting 
HTF Sections 2.4, 2.6, 2.9, BIS Sections 1.1,
1.2, 3.1, 3.2, MUR Sections 7.17.3, SSBD Section 9.2,


F 
Regularization, gradient descent Model complexity and overfitting, penalizing model complexity, description length, shrinkage methods, ridge regression, Lasso, gradient descent, 
HTF Section 3.3, BIS Sections 1.1, 1.3, 3.1.4, MUR Section 7.5


T 
Classification Introduction, classification as regression, linear classifiers, risk, conditional risk, logistic regression, MLE, surrogate loss, generalized additive models 
HTF Sections 4.1, 4.4, BIS Sections 1.5, 4.3.2, MUR Sections 8.18.3 

Th 
Logistic Regression Log odds ratio, logistic function, gradient descent, NewtonRaphson 
BIS Section 4.3.4, SSBD Sections 9.3, 14.1, MUR Section 8.5 

F 
Recitation: Convex Optimization Convex sets, convex function, standard form, Lagrange multipliers, equivalence of constrained and unconstrained versions of ridge regression and Lasso regression 
MRT Appendix B 

T 
Stochastic gradient descent Overfitting with logistic regression, MAP estimation, regularization, Softmax, Stochastic gradient descent (SGD) 
BIS Sections 7.1, SSBD Sections 15.115.1.1, MUR Sections 14.5 

Th 
Support Vector Machines Optimal separating hyperplane, Large margin classifier, margin and regularization, Lagrange multipliers, KKT conditions, maxmargin optimization, quadratic programming, support vectors 
HTF Section 4.5, MRT Sections 4.14.2 

F 
TBD



T 
Support Vector Machines II Nonseparable case, SVMs with slack, loss in SVM, solving SVM in the primal SVM regression 
BIS Section 7.1, SSBD Sections 15.115.1.1, MUR Section 14.5 

Th 
Kernel methods solving SVM in the primal, subgradient, subgradient descent, nonlinear features, feature space, kernel trick, representer theorem, kernel SVM in the primal, Mercer's kernels, radial basic function, kernel SVM, SVM regression 
BIS Sections 6.1, 6.2, MRT Sections 5.1, 5.2, 5.3.15.3.2, SSBD Sections 15.2, 15.415.5, MUR Sections 14.12 

F 
Recitation: classification Classification as regression: linear regression, logistic regression, SVMs, empirical risk minimization, losses 

T 
MIDTERM REVIEW 


Th 
MIDTERM



F 
Discuss MIDTERM solutions



Spring Break (3/18  3/22) 
