Astronomy 598 Topics in Theoretical Astrophysics (Machine Learning for Computational Science)

Winter 2017: Fri 11:00-12:20 Physics/Astronomy Building A210

Instructor: Pramod Gupta
psgupta *at* astro. washington. edu

Office hours: after class, or email.

Web-site: http://vpl.astro.washington.edu/users/psgupta/astro598machinelearning.html

Syllabus:

(1) Linear Models for regression (Maximum Likelihood Estimation, Least-squares)

(2) Linear Models for classification (Linear Discriminant Analysis, Logistic Regression, Maximum Likelihood Estimation, Gradient Descent)

(3) Linear Models and Regularization (Ridge Regression, Lasso)

(4) Kernel density estimation and classification (Naive Bayes Classifier)

(5) K-nearest neighbors and classification

(6) Model Complexity (Overfitting, underfitting, Bias-Variance tradeoff)

(7) Resampling (K-fold Cross-validation, Bootstrap)

(8) Cluster Analysis (hierarchical clustering, K-means, Gaussian mixtures)

(9) Principle Component Analysis

(10) Singular Value Decomposition

Grades:

Homework problems (100% of grade) will be assigned every week.

References:

The Elements of Statistical Learning
Second Edition
Hastie, Tibshirani, Friedman