COMS 4772 Advanced Machine Learning (Fall 2013)

Time & venue: Wednesday 4:10–6:00 PM in 545 Mudd.
Instructor: Daniel Hsu
Office hours: Friday 9:30–11:25 AM (or by appointment)
TA office hours: Monday 4–5 PM (Karl Stratos) and Tuesday 5–6 PM (Joan Cao), in the Mudd TA room
E-mails: {djhsu@cs.,stratos@cs.,zc2235@}<university domain>

Course information | Homework assignments | Final project

Date Topics and notes References and comments
Basic probabilistic techniques
9/4 Gaussian distributions, Markov's and Chebyshev's inequalities
[notes] (last updated 9/25)
  • Alon and Spencer, The Probabilistic Method, chapter on second moment method.
  • Please read sections 4.1.1–4.1.3 in the notes to complete the discussion on Gaussian distributions.
  • Homework 0 assigned.
9/11 Cramer-Chernoff bounding method, subgaussian and subgamma random variables
  • Alon and Spencer, The Probabilistic Method, appendix on large deviations.
  • Please read section 3.4 and the rest of section 4 in the notes (from last week) to complete the discussion on subgamma random variables and prepare for next week's lecture.
  • Homework 1 assigned.
Random linear embeddings
9/18 Random vectors, Johnson-Lindenstrauss lemma
[notes] (updated 9/19)
9/25 Constructions for fast JL embeddings
[notes] (updated 9/25)
10/2 Subspace-preserving embeddings, covering numbers, compressed sensing
10/9 Projection pursuit, locality-sensitive hashing
Spectral analysis
10/16 Covariance matrices, principal component analysis
10/23 Matrix tail inequalities, low-rank approximation
10/30 Power iteration, randomized low-rank approximation
11/6 Canonical correlation analysis, applications
Clustering models
11/13 Planted partition models
11/20 Mixture models
11/27
12/4

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