Difference between revisions of "CS4646 Spring 2018"

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|4 || 1/30 || Supervised Learning (KNN, LinReg, Decision Trees) ||
 
|4 || 1/30 || Supervised Learning (KNN, LinReg, Decision Trees) ||
 
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|5 || 2/6 || Ensembles (Bagging, Boosting) ||
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|5 || 2/6 || Ensembles (Bagging, Boosting), Market History, Actors || Project 2
 
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|6 || 2/13 || Market History, Actors, Order Types || Project 2
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|6 || 2/13 || Order Types, Order Book, Leverage ||
 
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|7 || 2/20 || Order Book, Leverage, Valuation ||  
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|7 || 2/20 || Valuation, Technical Analysis, Candlestick Chart Patterns || Project 3
 
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|8 || 2/27 || Technical Analysis, Candlestick Chart Patterns, Time Value of Money, CAPM || Project 3
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|8 || 2/27 || Time Value of Money, CAPM, Efficient Market Hypothesis ||  
 
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|9 || 3/6 || Efficient Market Hypothesis, Fund. Law of APM, Efficient Frontier ||
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|9 || 3/6 || Fund. Law of APM, Efficient Frontier, Review || Exam 1
 
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|10 || 3/13 || Review, Exam 1 || Exam 1
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|10 || 3/13 || Finite Automata, MDP, Value/Policy Iteration || Project 4 || Drop Day (3/14)
 
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|11 || 3/20 || Finite Automata, MDP, Value/Policy Iteration || Project 4
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|11 || 3/20 || SPRING BREAK ||
 
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|12 || 3/27 || Q-Learning ||
 
|12 || 3/27 || Q-Learning ||
 
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|13 || 4/3 || Misc Machine Learning / Catch-up || Project 5
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|13 || 4/3 || Q-Learning, Misc ML Topics or Catch-up || Project 5
 
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|14 || 4/10 || Options, Time Series Q-Learning ||
 
|14 || 4/10 || Options, Time Series Q-Learning ||

Revision as of 16:01, 10 January 2018

DRAFT

This page is still in progress. Don't consider it final until the first day of class.

Overview

You are on the page for information specific to the Spring 2018 session of CS 4646. Go here (Undergrad_ML4T) for overall course policies.

Schedule & Forum

  • Project Deadlines: All projects are due Sunday night at 11:55 PM Eastern US Time. Projects are due at the end of the week in which they are listed. For example, Project 1 (assess a portfolio) is listed as due in Week 3, meaning Sunday 1/28 -- the Sunday after Week 3.
  • Late Projects: As stated in class, projects will be accepted up to 24 hours late without any excuse required. Projects one second to 24 hours late will receive a -10 penalty. After 24 hours, late projects will not be accepted for any credit at all unless arrangements were made with the instructor prior to the project deadline.
  • Schedule: Subject to Change if necessary. I will give you as much notice as possible.


Week Date (Tues) Weekly Topics Due
1 1/9 Course Overview, Python/Pandas Tutorial
2 1/16 Numpy Tutorial, Visualizing Market Data, Working with Time Series, Incomplete Data
3 1/23 ML Lexicon/Taxonomy, Evaluating Learners Project 1
4 1/30 Supervised Learning (KNN, LinReg, Decision Trees)
5 2/6 Ensembles (Bagging, Boosting), Market History, Actors Project 2
6 2/13 Order Types, Order Book, Leverage
7 2/20 Valuation, Technical Analysis, Candlestick Chart Patterns Project 3
8 2/27 Time Value of Money, CAPM, Efficient Market Hypothesis
9 3/6 Fund. Law of APM, Efficient Frontier, Review Exam 1
10 3/13 Finite Automata, MDP, Value/Policy Iteration Project 4 Drop Day (3/14)
11 3/20 SPRING BREAK
12 3/27 Q-Learning
13 4/3 Q-Learning, Misc ML Topics or Catch-up Project 5
14 4/10 Options, Time Series Q-Learning
15 4/17 Review, Exam 2, Last regular day of class Exam 2
16 4/24 Final Instruction Days (Tuesday), Help Session on Final Project Project 6
17 5/1 Finals week, NO FINAL EXAM

Assignments

Projects (60%)

  • [assess_portfolio] 5% (easy)
  • Regression / Ensemble Learners 10% (challenging)
  • Market Simulator 10% (moderate)
  • Manual Strategy 10% (moderate)
  • Q-Learning Robot 10% (moderate)
  • Strategy Learner 15% (very challenging)

Exams (40%)

  • Exam 1: 20%
  • Exam 2: 20%

Exam Study Guides

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).