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UCLA Astronomy Machine Learning Reading group - 2017


Machine learning is a topic that has risen in prominence recently as we get more and more data. We are seeing techniques from machine learning used more widely in astronomy. The goal of this reading group is to become more familiar with topics in machine learning and its connections to statistical tools that are in use in Astronomy. The plan is to go through a couple of textbooks on machine learning and discuss the basic underlying principles and methods. It would be in the style of a reading group where everyone would read the same topic, but a presenter would rotate each meeting and present a topic with associated code implementing the algorithm.


Extra Readings

-Kirkpatrick, K. (2017). It's not the algorithm, it's the data. Communications of the ACM, 60(2), 21-23.

Code Samples

-- SciServer cosmology and astronomy Jupyter Notebook code samples


Potential topics this quarter:

  • Classification
  • Naive Bayes
  • Multinomial Bayes
  • Support Vector Machines
  • Ensemble
  • Random Forest
  • Decision Trees
  • Hierarchical clustering


Meetings will take place on Fridays at 11 am to Noon in PAB-4-330. Room changes will be sent via email.

Organizers: Tuan Do (@followthesheep), Bernie Randles (@brandles)

Date Topic Readings Presenter
2017-04-14 Introduction to Machine Learning Ch1 Goodfellow, Ch1 Kelleher, Install software B. Randles, T. Do
2017-04-21 Review of Probability Ch 6.1 & 6.2 Kelleher T. Do, G. Martinez
2017-04-28 Naive Bayes - Intro Ch 6.3, 6.4.1, 6.4.2 Kelleher, Problem 6 B. Randles, T. Do
2017-05-05 Naive Bayes - continued, LOCATION CHANGE: PAB3-703 Ch 6.4.1, 6.4.2, and 6.4.3 Kellher, Problem 6.3 A. Hees
2017-05-12 Introduction to Scikit-Learn, Hyperparameters and Model Validation Python Data Science Handbook, Ch. 5.2, Ch. 5.3 X. Wang, Y. Chiou
2017-05-19 Support Vector Machines Python Data Science Handbook, 5.7: In-Depth: Support Vector Machines, Supplementary Reading: Hands-On Machine Learning, Chapter 5 A. Gautam, D. Cohen, K. Kosmo
2017-05-26 Decision Trees Ch 4.1 to 4.4, Kelleher, Problems 1&2. Hands-On Machine Learning Ch 5 J. Salas, J. Zink
2017-06-02 Ensemble Learning & Random Forests Ch 4.4.5 Kelleher, Ch 4, Problem 5, Hands-On Machine Learning Ch 7 M. Topping, J. Ryan
2017-06-09 Principle Component Analysis Ch 8, Problem 9, Hands-On Machine Learning Ch 8 D. Chu