Difference between revisions of "CS7646 Summer 2017"

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(Created page with "==Overview== You are on the page for information specific to the Summer 2017 session of this course. Go here (Machine_Learning_for_Trading_Course) for overall course pol...")
 
 
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You are on the page for information specific to the Summer 2017 session of this course.  Go here ([[Machine_Learning_for_Trading_Course]]) for overall course policies.
 
You are on the page for information specific to the Summer 2017 session of this course.  Go here ([[Machine_Learning_for_Trading_Course]]) for overall course policies.
  
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==Important Note==
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==Specific On-Campus Information==
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'''This is currently a duplicate of Spring 2017, a work in progress for Summer 2017.'''
 
  
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==2017 Spring Schedule==
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* Any on-campus deviations (related to office hours, exams, lecture times/topics/notes) can be found here: [[CS7646_Summer_2017_ATL]]
  
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*  [[https://docs.google.com/spreadsheets/d/1dOk083xydbPoLNkZcNh-KqGkD9oztKuly8WaE_ai5JE/pubhtml?gid=0&single=true]]
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==2017 Summer Schedule (OMS)==
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This is a link to the schedule for the online (OMS) session of the course.  The project deadlines are the same for the on campus session.
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*  [[https://docs.google.com/spreadsheets/d/1U-VB_qEQAYzrEVQHG4a6xgrINd8vTZxByISNa-OXLsY/pubhtml?gid=0&single=true]]
  
 
==Assignments & Grading==
 
==Assignments & Grading==
  
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'''Projects (70%)'''
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'''Projects (62%)'''
  
 
* [[http://quantsoftware.gatech.edu/MC1-Project-1 MC1-Project-1: Assess portfolio]] 5% (easy)
 
* [[http://quantsoftware.gatech.edu/MC1-Project-1 MC1-Project-1: Assess portfolio]] 5% (easy)
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* [[http://quantsoftware.gatech.edu/MC1-Project-2 MC1-Project-2: Optimize a portfolio]] 5% (easy)
 
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* [[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-1 MC3-Project-1: Implement and assess a regression learner using decision trees and random forests]] 15% (hard)
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* [[http://quantsoftware.gatech.edu/MC2-Project-1 MC2-Project-1: Build a market simulator]] 15% (moderate)
 
* [[http://quantsoftware.gatech.edu/MC3-Project-2 MC3-Project-2: Q-learning maze navigation]] 10% (easy)
 
* [[http://quantsoftware.gatech.edu/MC3-Project-2 MC3-Project-2: Q-learning maze navigation]] 10% (easy)
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* [[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)
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* [[http://quantsoftware.gatech.edu/MC3-Project-4 MC3-Project-4: Trading Strategy Learner]] 17% (very hard)
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* [[http://quantsoftware.gatech.edu/MC3-Project-4 MC3-Project-4: Q-learning trader]] 10% (moderate)
 
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'''Extra credit project'''
 
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If you choose to do the extra credit project, you can gain up to 15 additional points applied to the Projects portion of the class.  In this case total projects grade is calculated as
 
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min(70, sum(project grades) + extra credit grade)
 
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Accordingly, you don't need to complete the extra credit project to earn a perfect projects score.  We strongly recommend that you focus on the first 7 projects only and skip the extra credit one.  However, if you bomb one of the other projects, if you want to challenge yourself, or if you need to make up points you can attempt VICIOUS RABBIT 2000. Note however that this last project is extremely hard. It has so far only been attempted by senior graduate students and the Black Knight. No one has succeeded to date.
 
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* [[http://quantsoftware.gatech.edu/MC3-Project-5 MC3-Project-5: Q-learning VICIOUS RABBIT 2000]] 15% (extremely hard)
 
  
 
'''Homework (5%):'''
 
'''Homework (5%):'''
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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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'''Exams (22%)'''
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'''Exams (30%)'''
  
 
* [[Midterm Study Guide]]
 
* [[Midterm Study Guide]]
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* [[Final Study Guide]]
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* [[Exam 2 Study Guide]]
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* Midterm 11%
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* Midterm 15%
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* Final 11%
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* Final 15%
  
 
'''Class participation (3%)'''
 
'''Class participation (3%)'''

Latest revision as of 20:34, 17 July 2017

Overview

You are on the page for information specific to the Summer 2017 session of this course. Go here (Machine_Learning_for_Trading_Course) for overall course policies.

Specific On-Campus Information

  • Any on-campus deviations (related to office hours, exams, lecture times/topics/notes) can be found here: CS7646_Summer_2017_ATL

2017 Summer Schedule (OMS)

This is a link to the schedule for the online (OMS) session of the course. The project deadlines are the same for the on campus session.

Assignments & Grading

Projects (62%)

Homework (5%):

Exams (30%)

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).

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