Title 
Version 
Date of Lecture 
General Topics 


1.
Fitting Linear Statistical Models to Data by Least Squares:
Introduction 
January 28 
January 28 
2.
Fitting Linear Statistical Models to Data by Least Squares:
Weighted 
January 29 
January 30 



Discovering Structure in Data 


1.
From Data to Graphs, Weighted Graphs and Graph Laplacians
Matlab highres figure ,
Matlab lowres figure ,
movie 
April 4 
February 4 
2. Geometric Graph Embeddings: (1) Full Data 
February 16 
February 11 
3. Geometric Graph Embeddings: (2) Partial Data 
February 25 
February 18 
4. Alignment Problems 
February 25 
February 25 
5.
Visualization and Continuous Object Transformation,

March 2 
March 3 
6.
Graph Embeddings. Spectral Theory
 March 5 
March 24 
7.
Random Graphs 
April 7 
March 31, April 7 
8.
Cheeger Constant and Spectral Gap 
April 16 
April 14 
9.
Community Detection: Spectral Methods and SDPs 
April 23 
April 23 
10.
Dimension Reduction and Data Embedding Techniques 
March 5 
April 28, May 5 
11.
Review of the Discovery Thread 
March 5 
May 5 



Portfolios that Contain Risky Assets 


1.
Risk and Reward 
February 6 
February 6 
2.
Covariance Matrices 
February 6 
February 6 
3.
Markowitz Portfolios 
February 6 
February 13 
4.
Markowitz Frontiers 
February 6 
February 13 
5.
Portfolios with RiskFree Assets 
February 6 
February 20 
6.
Long Portfolios and Their Frontiers 
February 6 
February 20 
7.
Long Portfolios with a Safe Investment 
February 6 
February 27 
8.
Limited Portfolios and Their Frontiers 
February 6 
February 27 
11.
Indenpendent, IdenticallyDistributed Models for Assets 
April 20 
April 2 
12.
Assessment of Indenpendent, IdenticallyDistributed Models 
April 20 
April 9 
13.
Indenpendent, IdenticallyDistributed Models for Portfolios 
April 20 
April 16 
14.
Kelly Objectives for Markowitz Portfolios 
April 21 
April 21 
15.
Cautious Objectives for Markowitz Portfolios 
May 3 
April 21 and 30 
16.
Optimization of MeanVariance Objectives 
May 3 
April 30 


