# eBook XXX Winter School:

summaries, talks, references, and tutorials

This webpage contains the materials needed to follow the lectures given during the school.

It contains a summary with the contents given by each one of the lectures (named as ** Lectures notes**), followed by the recorded talks (

**and the slides used for them (**

*Lecture #*)*. References, tutorials, and suggestions for additional reading are also included.*

**Lecture slides**)from the Organizers

## General overview on the use of machine learning techniques in astronomy

By: **Prof. S. George Djorgovski, ***Caltech, Division of Physics, Mathematics and Astronomy*

**Lecture 1:** A broad intro into astroinformatics in the context of data science

- Transformation of science driven by computing and information technology.
- From Virtual Observatory to Astroinformatics and beyond.
- Methodology transfer in data science.

**Lecture 2:** Data visualization

- Basics of data visualization.
- Using color.
- Multidimensional data visualization and Virtual Reality.

**Lecture 3:** Classification challenges in time domain astronomy, part 1

- The challenges of transient classification.
- A brief overview of the ML methodology and feature selection.
- Characterizing the light curves.

**Lecture 4:** Classification challenges in time domain astronomy, part 2

- Archival data mining in time domain
- Periodicity searches.
- Predictive data mining.
- Automated follow-up decision making.

## Challenges

By: **Prof. Mario Juric, ***University of Washington*

This series discusses data challenges and solutions in forthcoming large astronomical surveys. It covers the topic broadly, but with additional focus on Large Synoptic Survey Telescope (LSST) as the largest ground-based survey of the next decade, and the Zwicky Transient Facility (ZTF) the closest time-domain precursor to LSST which is operating today.

## Machine Learning: Unsupervised

By: **Dr. Dalya Baron, ***School of Physics and Astronomy, Tel-Aviv University*

**Lecture 2:** Decision Trees and Random Forests

**Lecture 3:** Dimensionality reduction algorithms

**Lecture 4:** Dimensionality reduction algorithms

**Lecture 4:** Outlier detection

**Lecture 4:** Summary: advanced concepts and algorithms, current status of the field, and open questions

### Tutorials and related materials

## Machine Learning: Supervised

By: **Prof. Michael Biehl,*** Bernoulli Institute for Mathematics and Computer Science, University of Groningen*

**Lecture 1:** Introduction

- Supervised learning, clasification, regression.
- Machine learning “vs.” statistical modeling.

**Lecture 2:** Perceptron

**Lecture 3:** Support Vector Machine

**Lecture 4:** Distance-based systems

### Tutorials and extra materials

## Deep Learning

By: **Prof. Marc Huertas-Company,*** Université Paris-Diderot - Observatoire de Paris; Instituto de Astrofísica de Canarias*

**Lecture 1:** An Introduction to Deep Learning for Astronomy

- Pereptron, neuron definition
- Layer of neurons, hidden layers
- Activation Functions
- Optimization (gradient descent)
- Backpropagation

**Lecture 2:** Deep Learning: convolutional neural networks for classification and regression

**Lecture 3:** Deep Learning: convolutional neural networks for classification and regression

- Convolutions as neurons
- CNNs (pooling, dropout)
- Vanishing Gradient / Batch normalization
- Data Augmentation
- Transfer Learning
- CNN as feature extractor for astronomy
- Optimizing your net: hyper parameter search
- Visualizing CNNs (deconvnets, inceptionism, integrated gradients)

**Lecture 4:** CNNs as Image Generators

- Variational Auto Encoders
- Generative Adversarial Networks
- U-nets