CS7646 Fall 2016

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Revision as of 12:06, 24 August 2016 by Tucker (talk | contribs) (→‎Overview)
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Overview

Please visit the main course page for an overall course introduction and policies: Machine_Learning_for_Trading_Course. The information on this page is specific to this semester.

2016 Fall Schedule

Assignments & Grading

  • [MC1-Project-1: Assess portfolio] 5%
  • [MC1-Project-2: Optimize a portfolio] 5%
  • MC3-Project-1: Implement and assess a regression learner using decision trees and random forests 15%
  • MC2-Project-1: Build a market simulator 15%
  • MC2-Homework-1: Create two midterm questions 5%
  • Midterm 20%
  • MC3-Project-2: Implement your own "manual" quant strategy, then do it with decision tree classification, compare 10%
  • MC3-Project-3: Q-learning maze navigation 10%
  • MC3-Project-4: Q-learning trader 15%
  • Piazza participation, up to 2% bonus for the most helpful contributors.

Thresholds:

  • A: 90% and above
  • B: 80% and above
  • C: 70% and above
  • D: 60% and above
  • F: below 60%

The projects linked to below are from previous semesters. We keep them here so you can peek ahead, but please keep in mind that they will be revised.