Difference between revisions of "CS7646 Fall 2017"
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==Assignments & Grading== | ==Assignments & Grading== | ||
Revision as of 12:17, 22 August 2017
Overview
You are on the page for information specific to the Fall 2017 session of this course. Go here (Machine_Learning_for_Trading_Course) for overall course policies.
OMSCS & On-Campus Information
This course is taught simultaneously on campus and via Udacity as part of the OMSCS program.
Schedule
- [[1]]
Assignments & Grading
Projects (62%)
- [MC1-Project-1: Assess portfolio] 5% (easy)
- [MC3-Project-1: Implement and assess a regression learner using decision trees and random forests] 15% (hard)
- [MC2-Project-1: Build a market simulator] 15% (moderate)
- [MC3-Project-2: Q-learning maze navigation] 10% (easy)
- [MC3-Project-4: Trading Strategy Learner] 17% (very hard)
Homework (5%):
Exams (30%)
- Midterm Study Guide
- Exam 2 Study Guide
- Midterm 15%
- Final 15%
Class participation (3%)
- Class participation is determined by activity on piazza.
Thresholds
- A: 90% and above
- B: 80% and above
- C: 70% and above
- D: 60% and above
- F: below 60%
These are hard boundaries (we round down).
