Course 2C: Modeling with Data and Uncertainty

by Daniel Sinderson

Course Description

This is course 2C in my DIY graduate program. It’s a year-long course in statistical modeling and machine learning for both time-series and cross-sectional data.

Course Materials

The primary text will be Foundations of Applied Mathematics vol. 3 by Jeffrey Humpherys, Tyler J. Jarvis, and Emily J. Evans.
This includes a freely available lab supplement.

Syllabus

#	Chapter	HW	Lab
1	Probability	Notes	-
2		Problem Sets	Information Theory
3	Markov Processes	Notes	-
4		Problem Sets	K-Means Clustering
5	Classical Inference	Notes	-
6		Problem Sets	-
7	Regression	Notes	-
8		Problem Sets	Linear and Logistic Regression
9	Graphical Models	Notes	-
10		Problem Sets	Metropolis-Hastings
11	Estimation in State-space Models	Notes	-
12		Problem Sets	Gaussian Mixture Models
13	Review of Multivariate Calculus and Optimization	Notes	-
14		Problem Sets	Discrete Hidden Markov Models
15	Machine Learning Basics	Notes	-
16		Problem Sets	Speech Recognition using CDHMMS
17	Unsupervised Learning	Notes	-
18		Problem Sets	Kalman Filter
19	Linear Models	Notes	-
20		Problem Sets	ARMA Models
21	Decision Trees	Notes	-
22		Problem Sets	Non-negative Matrix Factorization Recommender
23	Neural Networks	Notes	-
24		Problem Sets	Recurrent Neural Networks
25	Deep Learning	Notes	-
26		Problem Sets	Convolutional Neural Networks
27	Data Augmentation and Generation	Notes	-
28		Problem Sets	TBD
29	Reinforcement Learning	Notes	-
30		Problem Sets	TBD

Written on: December 1, 2024