Difference between revisions of "CS7646 Fall 2017"

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(Created page with "==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 polic...")
 
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This course is taught simultaneously on campus and via Udacity as part of the OMSCS program.
 
This course is taught simultaneously on campus and via Udacity as part of the OMSCS program.
  
==Schedule==
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==Schedule & Forum==
  
*  [[https://docs.google.com/spreadsheets/d/1U-VB_qEQAYzrEVQHG4a6xgrINd8vTZxByISNa-OXLsY/pubhtml?gid=0&single=true]]
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Schedule: [[https://docs.google.com/spreadsheets/d/1YOqAQ6S5KZw7rQEj6PNV248myLNz7RzKnmTEHTuwi5c/pubhtml?gid=0&single=true sheets.google.com]]
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* Forum: [[https://www.reddit.com/r/cs7646_fall2017/ Class forum at Reddit.com]]
  
 
==Assignments & Grading==
 
==Assignments & Grading==
  
'''Projects (62%)'''
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'''Projects'''
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* [[http://quantsoftware.gatech.edu/assess_portfolio assess_portfolio]] 2.5% (easy)
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* [[http://quantsoftware.gatech.edu/optimize_something optimize_something]] 2.5% (easy)
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* [[http://quantsoftware.gatech.edu/marketsim marketsim]] 10% (moderate)
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* [[http://quantsoftware.gatech.edu/defeat_learners defeat_learners]] 5% (easy)
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* [[http://quantsoftware.gatech.edu/assess_learners assess_learners]] 15% (challenging)
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* [[http://quantsoftware.gatech.edu/qlearning_robot qlearning_robot]] 10% (moderate)
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* [[http://quantsoftware.gatech.edu/manual_strategy manual_strategy]] 11% (moderate)
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* [[http://quantsoftware.gatech.edu/strategy_learner strategy_learner]] 16% (very challenging)
  
* [[http://quantsoftware.gatech.edu/MC1-Project-1 MC1-Project-1: Assess portfolio]] 5% (easy)
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'''Exams'''
* [[http://quantsoftware.gatech.edu/MC3-Project-1 MC3-Project-1: Implement and assess a regression learner using decision trees and random forests]] 15% (hard)
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* Exam 1: Paper exam on campus, via proctortrack for online students 12.5%
* [[http://quantsoftware.gatech.edu/MC2-Project-1 MC2-Project-1: Build a market simulator]] 15% (moderate)
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* Exam 2: Paper exam on campus, vis proctortrack for online students 12.5%
* [[http://quantsoftware.gatech.edu/MC3-Project-2 MC3-Project-2: Q-learning maze navigation]] 10% (easy)
 
* [[http://quantsoftware.gatech.edu/MC3-Project-4 MC3-Project-4: Trading Strategy Learner]] 17% (very hard)
 
 
 
'''Homework (5%):'''
 
 
 
* [[http://quantsoftware.gatech.edu/MC3-Homework-1 MC3-Homework-1: Generate datasets that defeat learners]] 5%
 
 
 
'''Exams (30%)'''
 
  
 
* [[Midterm Study Guide]]
 
* [[Midterm Study Guide]]
 
* [[Exam 2 Study Guide]]
 
* [[Exam 2 Study Guide]]
* Midterm 15%
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* Final 15%
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'''Exam contributions (0%)'''
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* This component has been removed for this semester.  The points have been redistributed to other assignments.
  
 
'''Class participation (3%)'''
 
'''Class participation (3%)'''
 +
* Class participation is determined by pop quizzes and activity on reddit.
  
* Class participation is determined by activity on piazza.
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'''Extra Credit (Up to 2%)'''
 +
* [[2017 Extra Credit]]
  
 
==Thresholds==
 
==Thresholds==
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These are hard boundaries (we round down).
 
These are hard boundaries (we round down).
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 +
[[https://docs.google.com/document/d/1_pZj99PKcMcCCsLb9dsvSFE9L6vjN96ZOXUVcLHIeJM/edit?usp=sharing]]

Latest revision as of 18:14, 2 January 2018

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 & Forum

Assignments & Grading

Projects

Exams

  • Exam 1: Paper exam on campus, via proctortrack for online students 12.5%
  • Exam 2: Paper exam on campus, vis proctortrack for online students 12.5%

Exam contributions (0%)

  • This component has been removed for this semester. The points have been redistributed to other assignments.

Class participation (3%)

  • Class participation is determined by pop quizzes and activity on reddit.

Extra Credit (Up to 2%)

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).

[[1]]