CS7646 Fall 2016
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.
