Difference between revisions of "CS7646 Spring 2017"
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==Assignments & Grading== | ==Assignments & Grading== | ||
| − | * [[http://quantsoftware.gatech.edu/MC1-Project-1 MC1-Project-1: Assess portfolio]] | + | Projects: |
| − | * [[http://quantsoftware.gatech.edu/MC1-Project-2 MC1-Project-2: Optimize a portfolio]] 5% | + | * [[http://quantsoftware.gatech.edu/MC1-Project-1 MC1-Project-1: Assess portfolio]] 5% (easy) |
| − | * [[http://quantsoftware.gatech.edu/MC3-Project-1 MC3-Project-1: Implement and assess a regression learner using decision trees and random forests]] 15% | + | * [[http://quantsoftware.gatech.edu/MC1-Project-2 MC1-Project-2: Optimize a portfolio]] 5% (easy) |
| − | * [[http://quantsoftware.gatech.edu/ | + | * [[http://quantsoftware.gatech.edu/MC2-Project-1 MC2-Project-1: Build a market simulator]] 10% (moderate) |
| + | * [[http://quantsoftware.gatech.edu/MC3-Project-1 MC3-Project-1: Implement and assess a regression learner using decision trees and random forests]] 15% (hard) | ||
| + | * [[http://quantsoftware.gatech.edu/MC3-Project-2 MC3-Project-2: Q-learning maze navigation]] 10% (easy) | ||
| + | * [[http://quantsoftware.gatech.edu/MC3-Project-3 MC3-Project-3: Implement a "manual" quant strategy, then do it with decision tree classification]] 15% (very hard) | ||
| + | * [[http://quantsoftware.gatech.edu/MC3-Project-4 MC3-Project-4: Q-learning trader]] 10% (moderate) | ||
| + | * [[http://quantsoftware.gatech.edu/MC3-Project-4 MC3-Project-5: Q-learning deathmatch 2000]] 15% (extremely hard) | ||
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* [[http://quantsoftware.gatech.edu/MC3-Homework-1 MC3-Homework-1: Generate datasets that defeat learners]] 5% | * [[http://quantsoftware.gatech.edu/MC3-Homework-1 MC3-Homework-1: Generate datasets that defeat learners]] 5% | ||
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* [[Midterm Study Guide]] | * [[Midterm Study Guide]] | ||
| − | * Midterm | + | * Midterm 10% |
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* Class participation: 2%. | * Class participation: 2%. | ||
Revision as of 09:59, 11 January 2017
Overview
You are on the page for information specific to the Spring 2017 session of this course. Go here (Machine_Learning_for_Trading_Course) for overall course policies.
2017 Spring Schedule
- [[1]]
Assignments & Grading
Projects:
- [MC1-Project-1: Assess portfolio] 5% (easy)
- [MC1-Project-2: Optimize a portfolio] 5% (easy)
- [MC2-Project-1: Build a market simulator] 10% (moderate)
- [MC3-Project-1: Implement and assess a regression learner using decision trees and random forests] 15% (hard)
- [MC3-Project-2: Q-learning maze navigation] 10% (easy)
- [MC3-Project-3: Implement a "manual" quant strategy, then do it with decision tree classification] 15% (very hard)
- [MC3-Project-4: Q-learning trader] 10% (moderate)
- [MC3-Project-5: Q-learning deathmatch 2000] 15% (extremely hard)
- Class 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.
