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<p>A toda la comunidad,</p>
<p>les compartimos información sobre el Curso de Machine Learning
que el Dr. Laerte Sodré (Universidad de São Paulo, Brasil) dictará
entre los días 28 de noviembre y 7 de diciembre en nuestra
Facultad. Este curso fue organizado en el marco de la designación
del Dr. Sodré como Profesor Visitante de la FCAG. <br>
</p>
<div class="moz-forward-container">Atentamente,</div>
<div class="moz-forward-container">Secyt-FCAG</div>
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<div>Introduction to Machine Learning in Astronomy<br>
La Plata, Nov 28th to Dec 7th, 2022<br>
<br>
The objective of this course is to give a general and
practical introduction to data science in Astronomy, with
focus on machine learning tools. We want to give some context
on statistical techniques used by astronomers and present
machine learning procedures useful for the analysis in
astronomy. We plan 4 classes, with one hour of "theory" and
one hour of applications using Python.<br>
<br>
To be approved in this course, after the classes the student
will have 10 days to present a report with an application of
ML techniques. All students will also make a 5-10 minutes
video describing the project and results. We will organize a
virtual meeting on a date to be determined for presentation of
the videos and discussion of all projects.</div>
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<div>Postdocs as well as PhD and undergraduate students of
Astronomy, Geophysics and Meteorology are welcome to attend
this course. Researchers are also welcome to attend but
priority will be given to postdocs and students. </div>
<div><br>
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<div>If you are interested in attending this course, please
contact Dr. Analía Smith Castelli (<a
href="mailto:asmith@fcaglp.unlp.edu.ar"
moz-do-not-send="true" class="moz-txt-link-freetext">asmith@fcaglp.unlp.edu.ar</a>).<br>
<br>
Program of the Lectures<br>
<br>
Lecture 1: Introduction to Statistics for Data Analysis in
Astronomy.<br>
Lab1: introduction to python and Jupyter notebooks descriptive
statistics. <br>
<br>
Lecture 2: Machine Learning: general concepts.<br>
Lab2: data exploration with non-parametric techniques.<br>
<br>
Lecture 3: Regression & Classification.<br>
Lab3: tools for regression and classification; applications on
real data the workflow of ML.<br>
<br>
Lecture 4: Deep Learning.<br>
Lab4: neural networks with the Keras/TensorFlow package and
Google Colab.<br>
<br>
References<br>
- Statistics, Data Mining, and Machine Learning in Astronomy,
Ivezić, Connolly, VanderPlas & Gray, 2014 (<a
href="https://www.astroml.org/" moz-do-not-send="true"
class="moz-txt-link-freetext">https://www.astroml.org/</a>)<br>
- An Introduction to Statistical Learning, James, Witten,
Hastie & Tibishirani, 2021 (<a
href="https://www.statlearning.com/" moz-do-not-send="true"
class="moz-txt-link-freetext">https://www.statlearning.com/</a>)<br>
- Deep Learning, Goodfellow, Bengio & Courville, 2016 (<a
href="https://www.deeplearningbook.org/"
moz-do-not-send="true" class="moz-txt-link-freetext">https://www.deeplearningbook.org/</a>)<br>
- Deep Learning with Python, Chollet, 2018 (<a
href="https://www.manning.com/books/deep-learning-with-python"
moz-do-not-send="true" class="moz-txt-link-freetext">https://www.manning.com/books/deep-learning-with-python</a>)<br>
- Modern Statistical Methods for Astronomy: With R
Applications , Feigelson & Babu, 2012<br>
- Bayesian Methods in Cosmology, Trotta, arXiv:1701.01467,
2017<br>
- The theory that would not die: How Bayes’ Rule Cracked the
Enigma Code, Hunted Down Russian Submarines, and Emerged
Triumphant from Two Centuries of<br>
Controversy, Sharon Bertsch Mcgrayne, 2011<br>
- Bayesian Methods for Hackers: Probabilistic Programming and
Bayesian Inference, Cameron Davidson-Pilon, 2015<br>
- Probabilistic Deep Learning with TensorFlow 2, Imperial
College London @ <a href="http://www.coursera.com"
moz-do-not-send="true">www.coursera.com</a><br>
- The Dawes Review 10: The impact of deep learning for the
analysis of galaxy surveys, M. Huertas-Company & F.
Lanusse, arXiv:2210.01813, 2022<br>
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<div>---------------</div>
<div><font size="1">Dra. Analia Smith Castelli</font></div>
<div><font size="1">Investigadora Independiente -
CONICET</font></div>
<div><font size="1">Instituto de Astrofisica de La
Plata, UNLP-CONICET</font></div>
<div><font size="1">Facultad de Ciencias Astronomicas
y Geofisicas, UNLP</font></div>
<div><font size="1">Paseo del Bosque s/n, La Plata,
Buenos Aires, Argentina (B1900FWA)</font></div>
<div><font size="1">email: <a
href="mailto:asmith@fcaglp.unlp.edu.ar"
target="_blank" moz-do-not-send="true"
class="moz-txt-link-freetext">asmith@fcaglp.unlp.edu.ar</a></font></div>
<div><font size="1">phone: +54 221 4236593 int. 1117</font></div>
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