Topic Index

These are not listed in the order of when they will be covered, or even the depth in which they will be covered (this is one course, after all). But these are all topics we will be touching on. Some will be covered in class, some in homework, some in lab, and some you will be expected to read on your own.

Expect this list to either shrink, or for some topics to be replaced, as the semester goes on!




Basic Stats and Monte Carlo

Frequentist Statistics

sampling methods

Maximum Likelihood and Risk

Machine Learning a model


Information Theory and Statistical mechanics

Combinatoric optimization and markov chains

Hidden variables and learning

Basic Bayesian Stats

Even more bayes

Machine Learning and Decision Making from a bayesian perspective


Convergence and Model checking

More sampling

From density models to regression

Model comparision and selection

Variational Algorithms

Non-IID temporal models

Covariance and Gaussian Processes

Long Running models in this course