UofC Statistical Learning Study Group

Public repository for the UofC Statistical Learning study group. The group met over the summer of 2019 for 12 presentations given by university students and staff on different topics. The objective of the group was to develop foundational knowledge and explore new developments in the fields of statistics and machine learning.

The group was sponsored by the GSA Quality Money Fund and The UofC Biomedical Engineering Graduate Student Commitee (BMEG).

Presenting Members:

Jordan Bannister
Deepthi Rajashekar
Lucas Lo Vercio
Anthony Winder
Anup Tuladhar
Luis Souto Maior
Matthias Wilms
Banafshe Felfeliyan
Van Anh Le
Sebastian Crites
Nagesh Subbanna
Bryce Besler

Meetings

We are planning to meet every second week (roughly) on Friday from 12-1pm beginning April 5. Food will be provided.

Course Outline

Date	Topic	Presenter	Room
Friday Apr. 5^rd	Information Theory	Jordan	1405A
Friday Apr. 26^th	Stochastic Processes	Deepthi	G732
Friday May. 3^rd	Decision Trees/Forests	Lucas	G643
Friday May. 17^th	Reinforcement Learning	Anthony	G643
Friday May. 31^st	Generative Models	Anup	G643
Friday Jun. 7^th	Interpretable Models	Luis	G732
Friday Jun. 14^th	Computational Anatomy	Matthias	G384
Friday Jun. 21^st	Recurrent Neural Networks	Banafshe	G382
Friday Jul. 12^th	Ensemble Methods	Ahn	G382
Friday Jul. 26^th	Geometric Deep Learning	Sebastian	G384
Friday Aug. 9^th	Probabilistic Graphical Models	Nagesh	G384
Friday Aug. 23^rd	Causal Inference	Bryce	G384

Curriculum

The content was selected according to the interests and knowledge of the presenting members. Related topics are (roughly) grouped together.

1 Information Theory

Information, entropy (conditional, joint, relative, differential), mutual information

2 Stochastic Processes

Random walk (levy process), brownian motion, gaussian process, markov process, martingale

3 Decision Trees/Forests

Decsion trees, bagging (bootstrap aggregation), random forests, information gain

4 Reinforcement Learning

Markov decision process, policy learning (brute force, monte carlo, Q-learning), exploration vs exploitation (multi-armed bandit problem)

5 Generative Models

VAE, GAN, deep belief network, style transfer.

6 Interpretable Models

Explainability vs interpretability; feature visualization; activation-based, gradient-based, and embedding-based methods.

7 Computational Anatomy

Diffeomorphisms (morphisms, isomorphisms, homeomorphisms, manifolds), diffeomorphism groups, matching/registration (LDMM)

8 Recurrent Neural Networks

Fully recurrent network, LSTM, training (supervised, reinforcement)

9 Ensemble Methods

Blending, bagging, boosting, stacking.

10 Geometric Deep Learning

Convolutions on graphs and manifolds, graph/manifold CNN's, laplacian eigenbasis decomposition of graphs and manifolds.

11 Probabilistic Graphical Models

Bayesian networks, markov networks, conditional independence, joint probability factorization, hidden markov models

12 Causal Inference

Association, causation, intervention, counterfactuals, instrumentals, Structural Causal Models (SCM)

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