SDS 385: Statistics Models for Big Data
08/25 : Syllabus [here]. Lecture 1 [here]. Quiz [Here]. Exam solutions [Here].
08/31 : Linear regression [Slides].
09/9 : Stochastic Gradient Descent [Slides].
Reading : Chapters 9.1 9.2 and 9.3 in [Convex Optimization book] by Boyd and Vandenberghe.
09/20 : Momentum methods and subgradients [Slides].
Reading : Nesterov's accelerated gradient vs Polyak's momentum here .
Reading : Stephen Boyd's notes. here .
09/22 : Adaptive gradients - introduction [Slides].
09/27 : Proximal methods [Slides].
09/24 : Duality [Slides].
Reading : A note on optimization and duality [Here].
Reading : Proximal methods [Here].
10/4 : Reading: Support vector machines [here].
10/4,6 : Large scale SVM optimization [Slides].
10/11 : PCA and Random Projections [Here].
10/18 : Approximate nearest neighbors-minhashing [Slides].
10/18,20 : Approximate nearest neighbors-LSH [Slides].
Reading : The Mining of Massive Datasets book-Chapter 3. here .
11/01 : KD trees [Here].
11/03 : Clustering [Here].
11/8,10,15 : Clustering and networks [Here].
11/17 : Random walks on graphs [Slides].
11/22 : Semisupervised learning [Slides].
11/22,27 : Graph representation learning [Slides].
11/27,1 : Topic models and NMF [Slides].
11/6 : Resampling [Slides].