### Teaching

#### Winter 2018: Mini-course on Statistics and Inference in Astrophysics

##### Syllabus

**Syllabus (pdf)**

See below for some further resources.

##### Lecture slides

**
Lecture 1
Lecture 2 (Fitting example)
Lecture 3 (MCMC demo, MCMC examples notebook)
Lecture 4**

##### Problem set

**Problem Set**: the Hubble constant from the local distance ladder

#### Fall 2017: AST1420: Galactic Structure and Dynamics

See the course website on GitHub

#### Winter 2016: Mini-course on Statistics and Inference in Astrophysics

##### Lecture notes

**Lecture 1**

**Lecture 2**

**Lecture 3**

**Lecture 4**

**Lecture 5**

##### Problem sets

##### Some resources

**Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data** textbook

**Figures from the textbook (with code)**

**Information Theory, Inference, and Learning Algorithms**, by David Mackay; see Part IV in particular

**Gaussian Processes for Machine Learning**, by Carl Edward Rasmussen and Christopher K. I. Williams

**Data analysis recipes: Fitting a model to data**

**Data analysis recipes: Probability calculus for inference**

More to be added as the course progresses

The lecture notes on this page are licensed under a Creative Commons Attribution 4.0 International License. Lecture notes on external websites linked to from this page are covered under licenses specified on those websites.

### contact info

email: bovy [at] astro.utoronto.ca

address:

Department of Astronomy and Astrophysics

University of Toronto

50 St. George Street

Toronto, ON M5S 3H4