Definitions
Linear Neural Networks
- Perceptron
- Adaline
- Stochastic Gradient Descent
Classifiers
- Logistic Regression
- Support Vector Machines
- Decision Trees
- K-Neighbors
Course Description
This course is intended for all graduate students and provides an introduction to galaxies and extragalactic astronomy. The emphasis is on making the connection between what we can observe about galaxies and the more fundamental physical properties that we would like to know about the galaxies themselves. We will cover the main techniques used for observing galaxies (imaging, spectroscopy, distance methods, extragalactic surveys), the observed properties (morphology, colors, luminosity functions, surface brightness profiles, kinematics, scaling relations, and spectral energy distributions), and the basic components that make up individual galaxies (stars, gas, dust, central black holes, dark matter) like our own Milky Way. The companion class, ASTR 616 - Galaxies II, builds on this foundation to explore the underlying principles of galaxy formation, galaxy evolution, and cosmology.