Attention conservation notice:Self-promoting notice of a class at a university you don't attend, on an arcane subject you're not that interested in, presuming background you don't have.

Coming terrifyingly soon:

- Conceptual Foundations of Statistical Learning (36-465/665), Spring 2021
*Description*: This course is an introduction to the core ideas and theories of statistical learning, and their uses in designing and analyzing machine-learning systems. Statistical learning theory studies how to fit predictive models to training data, usually by solving an optimization problem, in such a way that the model will predict well, on average, on new data. The course will focus on the key concepts and theoretical tools, at a mathematical level accessible to students who have taken 36-401, "modern regression" (or equivalent) and its pre-requisites. The course will also illustrate those concepts and tools by applying them to carefully selected kinds of machine learning systems (such as kernel machines).*Time and place*: Tuesdays and Thursdays 2:20--3:40 pm, Pittsburgh Time, via Zoom*Pre-requisites*: Undergraduates taking the course as 36-465 must have a C or better in 36-401. Graduate students taking it as 36-665 are expected to have similar background in the theory and practice of linear regression models, linear algebra, mathematical statistics, probability, and calculus in multiple variables.*Topics in brief*(subject to revision): Prediction as a decision problem; elements of statistical decision theory; "risk"; "probably approximately correct"; optimizing on training data; the origins of over-fitting; deviation inequalities; uniform convergence and concentration inequalities; measures of model complexity (Rademacher complexity, VC dimension, etc.); "algorithmic stability" arguments; optimizing noisy functions; regularization and its effects on model complexity; model selection; kernel machines; random-feature machines; mixture models; and some combination of stochastic-process prediction, sequential decision-making/reinforcement learning, and low-regret ("on-line") learning.*This course vs. alternatives*: Students wanting exposure to a broad range of learning algorithms and their applications would be better served by other courses, especially 36-462/662 ("data mining", "methods of statistical learning"), 10-301/601 ("introduction to machine learning") or 10-701 ("introduction to machine learning" for Ph.D. students). This class is for those who want a deeper understanding of the underlying principles. It will mean a lot more math than coding, and it won't help you move up a leader-board, but it will help you understand the statistical reasons*why*learning machines work (when they do).

I have until classes begin on 1 February to figure out how I am actually going to make this happen.

Posted at November 24, 2020 18:25 | permanent link