Difference between revisions of "CS7646 Fall 2016"

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==Overview==
 
==Overview==
  
Please visit the main course page for an overall course introduction and policies: [Machine_Learning_for_Trading_Course].  The information on this page is specific to this semester.
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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==
 
==2016 Fall Schedule==
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==Assignments & Grading==
 
==Assignments & Grading==
  
* [[http://quantsoftware.gatech.edu/MC1-Project-1 MC1-Project-1: Assess portfolio]] 5%
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* [[http://quantsoftware.gatech.edu/MC1-Project-1 MC1-Project-1: Assess portfolio]] 4%
 
* [[http://quantsoftware.gatech.edu/MC1-Project-2 MC1-Project-2: Optimize a portfolio]] 5%
 
* [[http://quantsoftware.gatech.edu/MC1-Project-2 MC1-Project-2: Optimize a portfolio]] 5%
* MC3-Project-1: Implement and assess a regression learner using decision trees and random forests 15%
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* [[http://quantsoftware.gatech.edu/MC3-Project-1 MC3-Project-1: Implement and assess a regression learner using decision trees and random forests]] 15%
* MC2-Project-1: Build a market simulator 15%
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* [[http://quantsoftware.gatech.edu/MC2-Homework-1 MC2-Homework-1: Create a Machine Learning midterm question]] 2%
* MC2-Homework-1: Create two midterm questions 5%
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* [[http://quantsoftware.gatech.edu/MC3-Homework-1 MC3-Homework-1: Generate datasets that defeat learners]] 5%
* Midterm 20%
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* [[http://quantsoftware.gatech.edu/MC2-Project-1 MC2-Project-1: Build a market simulator]] 15%
* MC3-Project-2: Implement your own "manual" quant strategy, then do it with decision tree classification, compare 10%
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* [[Midterm Study Guide]]
* MC3-Project-3: Q-learning maze navigation 10%
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* Midterm 15%
* MC3-Project-4: Q-learning trader 15%
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* [[http://quantsoftware.gatech.edu/MC3-Project-2 MC3-Project-2: Q-learning maze navigation]] 10%
* Piazza participation, up to 2% bonus for the most helpful contributors.
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* [[http://quantsoftware.gatech.edu/MC3-Project-3 MC3-Project-3: Implement your own "manual" quant strategy, then do it with decision tree classification, compare]] 15%
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* [[http://quantsoftware.gatech.edu/MC3-Project-4 MC3-Project-4: Q-learning trader]] 12% (replacement for Final Exam)
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* Class participation: 2%.
  
 
Thresholds:
 
Thresholds:
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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.
 
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.
  
* [[http://quantsoftware.gatech.edu/MC2-Project-1 MC2-Project-1: Build a market simulator]]
 
 
* [[http://quantsoftware.gatech.edu/MC2-Project-2 MC2-Project-2: Implement bollinger bands, and create a simple trading strategy]]
 
* [[http://quantsoftware.gatech.edu/MC2-Project-2 MC2-Project-2: Implement bollinger bands, and create a simple trading strategy]]
 
* [[http://quantsoftware.gatech.edu/MC2-Homework-1 MC3-Homework-1: Create a Finance midterm question]]
 
* [[http://quantsoftware.gatech.edu/MC2-Homework-1 MC3-Homework-1: Create a Finance midterm question]]
* [[Midterm Study Guide]]
 
 
* [[http://quantsoftware.gatech.edu/MC3-Project-1 MC3-Project-1]]
 
* [[http://quantsoftware.gatech.edu/MC3-Project-1 MC3-Project-1]]
 
* [[http://quantsoftware.gatech.edu/MC3-Project-2 MC3-Project-2]]
 
* [[http://quantsoftware.gatech.edu/MC3-Project-2 MC3-Project-2]]
 
* [[http://quantsoftware.gatech.edu/MC3-Project-3 MC3-Project-3]]
 
* [[http://quantsoftware.gatech.edu/MC3-Project-3 MC3-Project-3]]
 
==Logistics==
 
 
* We will use Udacity for lecture videos.
 
** Login here using your GT account: [https://login.gatech.edu/cas/login?service=http%3A%2F%2Fweb.iam.gatech.edu%2Fudacity-login%2F GT-Udacity Login] ([https://www.youtube.com/watch?v=pyqirZW_sT8 instruction video])<br />
 
** Go to the course on Udacity (or navigate through My Courses): https://www.udacity.com/course/viewer#!/c-ud501
 
* We will use T-Square for submission of code and reports: [https://t-square.gatech.edu/portal T-Square] (pick appropriate course site)
 
* We will use Piazza for interaction and discussion: [https://piazza.com/ Fall 2016 Piazza forum]
 
 
==Minimum technical requirements==
 
 
* Browser and connection speed: An up-to-date version of Chrome or Firefox is strongly recommended. We also support Internet Explorer 9 and the desktop versions of Internet Explorer 10 and above (not the metro versions). 2+ Mbps recommended; at minimum 0.768 Mbps download speed.
 
 
* Hardware: A computer with at least 4GB of RAM and CPU speed of at least 2.5GHz.
 
 
* OS:
 
** PC: Windows XP or higher with latest updates installed
 
** Mac: OS X 10.6 or higher with latest updates installed
 
** Linux: Any recent distribution that has the supported browsers installed
 
 
==Office hours==
 
 
To be determined.
 
 
==Plagiarism==
 
 
In most cases I expect that all submitted code will be written by you. I will present some libraries in class that you are allowed to use (such as pandas and numpy). Otherwise, all source code, images and write-ups you provide should have been created by you alone.
 
 
==Late Policy & Absences==
 
 
Assignments are due at 11:55PM Eastern Time on the assignment due date.  Assignments turned in after 11:55PM are considered late.  Assignments may be turned in up to one day late with a 10% penalty.
 
 
Exams will be held on specific days at specific times.  If there is an emergency or other issue that requires changing the date of an exam for you, you will need to have it approved by the Dean of Students.  You can apply for that here:
 
 
* http://www.deanofstudents.gatech.edu (under Resources -> Class Absences)
 
 
==Legacy==
 
 
* Legacy: [[https://docs.google.com/spreadsheets/d/1JlhlQ1D4bmwP6THAcVKGo8PNxWjbUQazFeJl8Mz-ZSw/pubhtml old schedule]]
 

Latest revision as of 21:44, 28 November 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

  • 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.