CS7646 Fall 2016

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Overview

You are on the page for information specific to the Fall 2016 session of this course. Go here (Machine_Learning_for_Trading_Course) for overall course policies.

2016 Fall Schedule

Assignments & Grading

  • [MC1-Project-1: Assess portfolio] 4%
  • [MC1-Project-2: Optimize a portfolio] 5%
  • MC3-Project-1: Implement and assess a regression learner using decision trees and random forests 15%
  • MC3-Homework-1: Generate datasets that defeat learners (5%)
  • MC2-Project-1: Build a market simulator 15%
  • MC2-Homework-1: Create two midterm questions 4%
  • Midterm 15%
  • 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% (replacement for Final Exam)
  • Piazza participation: 2%.

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.