EE 290: Materials

Lectures

The Lecture notes will be continually updated as the quarter progresses. Each student is required to participate in scribing one lecture using LaTeX. The template can be downloaded at Scribe Template.

Each student will be involved in scribing a lecture individually. To ensure that the notes will be available for students currently in the course, scribed notes are due within one week after lecture (no late submissions accepted). Please use this Google Doc to reserve lectures for scribing. Note: the link is updated as here .

• Lecture 1 (Statistical Decision Theory)

• Lecture 2 (Matrix Decompositions, Norms and Perturbation)

• Lecture 3 & 4 (Random Matrices and Hidden Clique in Random Graph)

• Lecture 5 & 6: Please refer to Section 6 in http://www.stat.yale.edu/~yw562/teaching/stats-graphs.pdf

• Lecture 7: Please refer to Section 7 in http://www.stat.yale.edu/~yw562/teaching/stats-graphs.pdf

• Lecture 8: (Exact recovery of stochastic block model)

• Lecture 9&10: (Spectral Method for Exact Recovery of SSBM)

• Lecture 11: Please refer to Section 9 in http://www.stat.yale.edu/~yw562/teaching/stats-graphs.pdf

• Lecture 12: (Correlated Recovery I)

• Lecture 13: Please refer to Section 8 in http://www.stat.yale.edu/~yw562/teaching/stats-graphs.pdf

• Lecture 14: (Grothendieck Inequality and its Application in SBM)

• Lecture 15: Please refer to Section 12 in http://www.stat.yale.edu/~yw562/teaching/stats-graphs.pdf

• Lecture 16&17: (Adaptive Estimation)

• Lecture 18&19: (Nonparametric Function Estimation)

• Lecture 20&21: (Wavelet Shrinkage)

• Lecture 22: (Robust Location Estimation)

• Lecture 23: (Robust Location Estimation Cont'd)

• Lecture 24: (Robust Hypothesis testing between TV balls)