Date 
Topic 
Assignment 
Tue 8.23 
Class Overview 

Thu 8.25 
Probability Review : Sample Space, Random Variables, Independence 

Tue 8.30 
Probability Review : PDFs, CDFs, Expectation, Variance, Joint and Marginal Distributions 
HW1 out 
Thu 9.01 
Bayes Rule 

Tue 9.06 
Bayes Rule : Bayesian Reasoning 

Thu 9.08 
Convergence : Central Limit Theorem and Estimation 

Tue 9.13 
Convergence : PAC Algorithms and Concentration of Measure 
HW 1 due 
Thu 9.15 
Linear Algebra Review :
Vectors, Matrices, Multiplication and Scaling 
Quiz 1 
Tue 9.20 
Linear Algebra Review :
Norms, Linear Independence, Rank 
HW 2 out 
Thu 9.22 
Linear Algebra Review :
Inverse, Orthogonality, numpy 

Tue 9.27 
Linear Regression :
dependent, independent variables 

Thu 9.29 
Linear Regression :
multiple regreesion, polynomial regression 

Tue 10.04 
Linear Regression :
overfitting and crossvalidation 
HW 2 due 
Thu 10.06 
Linear Regression :
(slack) or kernels 
Quiz 2 
Tue 10.11 
FALL BREAK 

Thu 10.13 
FALL BREAK 

Tue 10.18 
Gradient Descent :
functions, minimum, maximum, convexity 
HW 3 out 
Thu 10.20 
Gradient Descent :
gradients and algorithmic variants 

Tue 10.25 
Gradient Descent :
fitting models to data and stochastic gradient descent 

Thu 10.27 
PCA :
SVD 

Tue 11.01 
PCA :
oops  retroactively class was canceled 

Thu 11.03 
PCA :
rankk approximation and eigenvalues 
HW 3 due 
Tue 11.08 
PCA :
power method  Election Day  don't forget to vote' 
HW 4 out 
Thu 11.10 
PCA :
centering, MDS, and dimensionalty reduction 
Quiz 3 
Tue 11.15 
Clustering :
Voronoi Daigrams 

Thu 11.17 
Clustering :
kmeans 

Tue 11.22 
Clustering :
EM 
HW 4 due 
Thu 11.24 
THANKSGIVING 
HW 5 out 
Tue 11.29 
Classification :
Linear prediction 

Thu 12.01 
Classification :
Perceptron Algorithm 

Tue 12.06 
Classification :
variants (kernels, KNN, maybe neural nets) 

Thu 12.08 
in class Review 
Quiz 4 
Fri 12.09 

HW 5 due 
Mon 12.12 
FINAL EXAM (10:30am  12:30pm) 
(practice) 