Date 
Topic 
Assignment 
Mon 8.21 
Class Overview 

Wed 8.23 
Probability Review : Sample Space, Random Variables, Independence 

Mon 8.28 
Probability Review : PDFs, CDFs, Expectation, Variance, Joint and Marginal Distributions 
HW1 out 
Wed 8.30 
Bayes Rule 

Mon 9.04 
LABOR DAY 

Wed 9.06 
Bayes Rule : Bayesian Reasoning 

Mon 9.11 
Convergence : Central Limit Theorem and Estimation 

Wed 9.13 
Convergence : PAC Algorithms and Concentration of Measure 
HW 1 due 
Mon 9.18 
Linear Algebra Review :
Vectors, Matrices, Multiplication and Scaling 
Quiz 1 
Wed 9.20 
Linear Algebra Review :
Norms, Linear Independence, Rank 
HW 2 out 
Mon 9.25 
Linear Algebra Review :
Inverse, Orthogonality, numpy 

Wed 9.27 
Linear Regression :
dependent, independent variables 

Mon 10.02 
Linear Regression :
multiple regreesion, polynomial regression 
HW 2 due 
Wed 10.04 
Linear Regression :
mini review + slack 
Quiz 2 
Mon 10.11 
FALL BREAK 

Wed 10.13 
FALL BREAK 

Mon 10.16 
Linear Regression :
overfitting and crossvalidation 
HW 3 out 
Wed 10.18 
Gradient Descent :
functions, minimum, maximum, convexity 

Mon 10.23 
Gradient Descent :
gradients and algorithmic variants 

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

Mon 10.30 
PCA :
SVD 

Wed 11.01 
PCA :
rankk approximation and eigenvalues 
HW 3 due 
Mon 11.06 
PCA :
power method 
HW 4 out 
Wed 11.08 
PCA :
centering, MDS, and dimensionalty reduction 

Mon 11.13 
Clustering :
Voronoi Daigrams 
Quiz 3 
Wed 11.15 
Clustering :
kmeans 

Mon 11.20 
Clustering :
EM, Mixture of Gaussians, MeanShift 

Wed 11.22 
Classification :
Linear prediction 
HW 4 due 
Mon 11.27 
Classification :
Perceptron Algorithm 
HW 5 out 
Wed 11.29 
Classification :
Kernels and SVMs 

Mon 12.04 
Classification :
Neural Nets 
Quiz 4 
Wed 12.06 
Inclass review 

Fri 12.08 

HW 5 due 
Tue 12.12 
FINAL EXAM (3:30pm  5:30pm) 
(practice) 
Thu 12.14 

HW 4R due 