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Session 19

The nineteenth session of the LSSTC DSFP was hosted by Drexel University in September 2023 and the curriculum covered Machine Learning.

The guest instructors for the S19 were:
Viviana Acquaviva :octocat:
John Wu :octocat:
Niharika Sravan :octocat:
Vicki Toy-Edens :octocat:

Additional lectures were given by the DSFP leadership team:
Bryan Scott :octocat:
Adam Miller :octocat:
Lucianne Walkowicz :octocat:

Schedule

Day 0 – The Beginning | Introduction for the New Cohort

"The future ain't what it used to be."

~ Yogi Berra

Two orientation lectures are provided asynchronously, these are:

  • A Brief Introduction to git/GitHub; B Scott
  • Building Visualizations Via Principles of Design ; A Miller

Saturday, Sep 9, 2023

  • 10:30 AM - 11:00 AM Registration & Introductions,
  • 11:00 AM - 11:30 AM Incoming Student Survey
  • 11:30 AM - 12:15 PM Introduction to the Vera C Rubin Observatory and Legacy Survey of Space & Time; L. Walkowicz
  • 12:15 PM - 12:30 PM Goals of the DSFP; B. Scott
  • 12:30 PM - 01:30 PM LUNCH (provided) & Discussion of the Code of Conduct; B. Scott
  • 01:30 PM - 02:45 PM Probability and Data Solutions; A. Miller
  • 02:45 PM - 04:00 PM Introduction to Bayesian Statistics Solutions; B. Scott
  • 04:00 PM - ??? Break

Day 1 – An Introduction to Machine Learning & Unsupervised Learning

"42."

~ Deep Thought on the answer to life, the universe, and everything (The Hitchhiker's Guide to the Galaxy).

Sunday, Sep 10, 2023

  • 09:00 AM – 09:30 AM o Introduction of Cohort 7 and the new instructors
  • 09:30 AM – 09:45 AM o Introduction to Hack Sessions
  • 09:45 AM – 10:30 AM o Lecture I – Introduction to Machine Learning; B. Scott
  • 10:30 AM – 11:00 AM o BREAK
  • 11:00 AM – 12:15 PM o Problem | Solutions I – Introduction to ML; B. Scott
  • 12:15 PM – 01:45 PM o LUNCH
  • 01:45 PM – 02:30 PM o Lecture II – Introduction to Unsupervised Learning; A. Miller
  • 02:30 PM – 03:30 PM o Problem II – Introduction to Unsupervised Learning; A. Miller
  • 03:30 PM – 03:40 PM o BREAK
  • 04:00 PM – 05:00 PM o Lecture III – Introduction to Dimensionality Reduction; B. Scott
  • 05:00 PM - 06:00 PM o Problem | Solutions III – Introduction to Dimensionality Reduction; B. Scott

Day 2 – Supervised Machine Learning, Tree, & Ensemble Methods

"I have an infinite capacity for knowledge, and even I'm not sure what is going on outside..."

~GladOS (Portal)

Monday, Sep 11, 2023

  • 09:00 AM – 10:30 AM o Lecture IV – Introduction to Supervised Machine Learning; V. Acquaviva
  • 10:30 AM – 11:00 AM o BREAK
  • 11:00 AM – 12:00 PM o Problem – Introduction to Supervised Machine Learning; V. Acquaviva
  • 12:00 PM - 01:30 PM o LUNCH
  • 01:30 PM – 02:30 PM o Lecture V – Tree & Ensemble Methods; V. Acquaviva
  • 02:30 PM – 03:30 PM o Problem: Tree & Ensemble Methods; V. Acquaviva
  • 03:30 PM - 04:00 PM o BREAK
  • 04:00 PM - 05:30 PM o Lecture VI – Building Perceptrons for Classification; A. Miller
  • 06:00 PM - ??:?? PM o Group dinner

Day 3 — Convolutional Neural Networks

"I am capable of distinguishing over one hundred and fifty simultaneous compositions. But in order to analyze the aesthetics, I try to keep it to ten or less."

~ Lt. Cmdr. Data (Star Trek: The Next Generation)

Tuesday, Sep 12, 2023

  • 09:00 AM - 10:00 AM o Lecture VII – Convolutional Neural Networks, J. Wu
  • 10:00 AM - 10:30 AM o BREAK
  • 10:30 AM - 12:00 PM o Problem: Convolutional Neural Networks J. Wu
  • 12:00 PM - ??:?? PM o BREAK

Day 4 — Graph Neural Networks and Reinforcement Learning

"It seems you feel our work is not of benefit to the public."

~ Rachael (Blade Runner)

Wednesday, Sep 13, 2023

  • 09:00 AM – 10:00 AM o Lecture VIII – Graph Neural Networks; J. Wu
  • 10:00 AM – 10:30 AM o BREAK
  • 10:30 AM – 12:00 PM o Problem: Graph Neural Networks; J. Wu
  • 12:00 PM – 01:30 PM o LUNCH
  • 01:30 PM – 02:30 PM o Lecture IX – Introduction to Reinforcement Learning and The Upper Confidence Bound; A. Sravan
  • 02:30 PM – 04:00 PM o Problem: The Upper Confidence Bound; A. Sravan
  • 04:00 PM – 04:30 PM o BREAK
  • 04:30 PM – 05:00 PM o Hack Pitch Session

Day 5 — Reinforcement Learning (cont.) & Hack Session

"The 9000 series is the most reliable computer ever made. No 9000 computer has ever made a mistake or distorted information. We are all, by any practical definition of the words, foolproof and incapable of error."

~ HAL 9000 (2001: A Space Odyssey)

Thursday, Sep 14, 2023

  • 9:00 AM - 10:00 AM o Lecture X – Thompson Sampling; A. Sravan
  • 10:00 AM – 10:45 AM o Problem: Thompson Sampling; A. Sravan
  • 10:45 AM – 11:00 AM o BREAK
  • 11:00 AM – 12:00 PM o Lecture XI – Professional Development: CV Workshop; V. Toy-Edens
  • 12:00 PM – 01:00 PM o LUNCH
  • 01:00 PM – 04:30 PM o Hack Session;
  • 04:30 PM – 05:00 PM o Hack tag–up & Meeting wrap up