Mathematics of Big Data

Notice

1) Each homework has both pdf and tex versions. To have the tex files successfully compiled, make sure that you have downloaded both macros.tex and hmcpset.cls and put them and the hw tex file under same folder.
If you have any questions with regard to the compilation of the tex files, feel free to ask the grutors for help.

2) For each coding problem, please submit your code to GitHub; please print out any graph or printing statements and submit them with the written part.

HOMEWORK

HW Description Starter Files Solution
Homework 1 (pdf)
Homework 1 (tex)
HW1 Files HW1 Solution
Homework 2 (pdf)
Homework 2 (tex)
HW2 Files HW2 Solution
Homework 3 (pdf)
Homework 3 (tex)
No starter file for Homework 3 HW3 Solution
Homework 4 (pdf)
Homework 4 (tex)
HW4 Files HW4 Solution
Homework 5 (pdf)
Homework 5 (tex)
No starter file for Homework 5 HW5 Solution
Homework 6 (pdf)
Homework 6 (tex)
HW6 Files HW6 Solution
Homework 7 (pdf)
Homework 7 (tex)
HW7 Files HW7 Solution
Homework 8 (pdf)
Homework 8 (tex)
HW8 Files HW8 Solution

LECTURE SLIDES

Dates Lecture Slides
Jan 27 Lecture 1: Overview of Big Data and its Analytics using Linear Regression
Feb 3 Lecture 2: Optimization Logistic and Generalized Linear models
Feb 10 Lecture 3: Review Probability, Schur Complement, Covariance Matrix and Multivariate Gaussian Distribution
Feb 17 Lecture 4: PCA, Dimensionality Reduction, Spectral Decomposition, SVD, Generative Learning Algorithm and Gaussian Discriminant Analysis
Feb 24 Lecture 5:Naive Bayes, L1-regularization, Sparsity, Lasso, SVM, and Kernel Method
Mar 2 Lecture 6: Unsupervised Learning, K-mean Clustering, Gaussian Mixture, Jensen's inequality and EM
Mar 9 Lecture 7: Kernel PCA, One Class SVMs and Learning Theory
Mar 30 Lecture 8: Bayesian Learning, Bayesian Logistic and Linear Regressions (review), Bayesian Inference, Intractable Integrals and Motivation for Approximate Methods and Learning Theory
Apr 6 Lecture 9: Recommender System, Collaborative Filtering, and Topic Modeling Based on Non-negative Matrix Factorization
Workshop/Additional Materials Workshop: Network Data Modeling

OTHERS