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.
- Fundamentals of Machine Learning for Predictive Data Analytics - Kelleher, J.; Namee, B.; D'Arcy, A.
- Deep Learning by Goodfellow, I.; Bengio, Y.; Courville, A.
- Python Data Science Handbook by VanderPlas, J.
- Hands-On Machine Learning with Scikit-Learn and TensorFlow
- Safari Books Online: http://proquest.safaribooksonline.com/book/programming/9781491962282
- github page: https://github.com/ageron/handson-ml
- Data Analysis: A Bayesian Tutorial - Sivia, D. & Skilling, J. - Not part of regular reading, but this is a useful reference for Bayesian statistics
-Kirkpatrick, K. (2017). It's not the algorithm, it's the data. Communications of the ACM, 60(2), 21-23. https://doi.org/10.1145/3022181
-- SciServer cosmology and astronomy Jupyter Notebook code samples https://github.com/sciserver/Notebooks
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 |
- A. Dehghanfar @arezud
- A. Gautam @abhimat
- A. Hees @auhees
- B. Randles @brandles
- B. Hansen
- C. Tsai
- D. Cohen @dcohen123
- D. Chu @dchu808
- E. Martin
- G. Martinez @gregorydavidmartinez
- I. Pasquetto @irenepasquetto
- J. Ryan @Jamieryan
- J.-L. Margot @jeanlucmargot
- J. Zink @jzink123
- J. Salas @jesusms007
- J. Samani
- K. Kosmo @kkosmo
- L. Jensen @lizjensengit
- M. Topping @michaeltopping
- M. Fitzgerald @astrofitz
- M. Golshan @milenasg
- S. Sakai
- T. Do @followthesheep
- X. Wang @albertfxwang
- Y. Chiou @yschiou