CS7646 Fall 2016
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.
