Difference between revisions of "CS4646 Spring 2018"

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{| class="wikitable"
 
{| class="wikitable"
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! Week !! Tuesday Class Date !! Topic !! Due
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! Week !! Date (Tues) !! Weekly Topics !! Due
 
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|1 || 1/9 || Course Overview, Intro to Markets, Intro to Machine Learning, Pandas Tutorial ||
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|1 || 1/9 || Course Overview, Python/Pandas Tutorial ||
 
|-
 
|-
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|2 || 1/16 || Visualizing Market Data, Pricing Stocks, Numpy Tutorial, Working with Time Series ||  
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|2 || 1/16 || Numpy Tutorial, Visualizing Market Data, Working with Time Series, Incomplete Data ||  
 
|-
 
|-
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|3 || 1/23 || Incomplete Data, Plots, ML Lexicon/Taxonomy, Evaluating Learners ||MC1-P1
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|3 || 1/23 || ML Lexicon/Taxonomy, Evaluating Learners || Project 1
 
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|4 || 1/30 || Supervised Learning (KNN, LinReg, Decision Trees, Boosting, Bagging) ||
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|4 || 1/30 || Supervised Learning (KNN, LinReg, Decision Trees) ||
 
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|5 || 2/6 || Market History, Actors, Order Book, Order Types ||MC3-P1 + MC3-HW1
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|5 || 2/6 || Ensembles (Bagging, Boosting) ||
 
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|6 || 2/13 || Markets, Valuation, Capitalization, Time Value of Money ||Exam 1
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|6 || 2/13 || Market History, Actors, Order Types || Project 2
 
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|7 || 2/20 || Options, Leverage ||MC2-P1
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|7 || 2/20 || Order Book, Leverage, Valuation ||  
 
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|8 || 2/27 || Technical Analysis, Candlestick Chart Patterns, CAPM, Efficient Market Hypothesis ||
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|8 || 2/27 || Technical Analysis, Candlestick Chart Patterns, Time Value of Money, CAPM || Project 3
 
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|9 || 3/6 || Fund. Law of APM, Efficient Frontier, Finite Automata, Markov Decision Processes ||MC3-P2
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|9 || 3/6 || Efficient Market Hypothesis, Fund. Law of APM, Efficient Frontier ||
 
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|10 || 3/13 || Value Iteration, Q-Learning || Exam 2
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|10 || 3/13 || Review, Exam 1 || Exam 1
 
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|11 || 3/20 || Final class, extra topics ||Final project (MC3-P3)
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|11 || 3/20 || Finite Automata, MDP, Value/Policy Iteration || Project 4
 
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|12 || 3/27 || Final class, extra topics ||Final project (MC3-P3)
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|12 || 3/27 || Q-Learning ||
 
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|13 || 4/3 || Final class, extra topics ||Final project (MC3-P3)
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|13 || 4/3 || Misc Machine Learning / Catch-up || Project 5
 
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|14 || 4/10 || Final class, extra topics ||Final project (MC3-P3)
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|14 || 4/10 || Options, Time Series Q-Learning ||
 
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|15 || 4/17 || Final class, extra topics ||Final project (MC3-P3)
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|15 || 4/17 || Review, Exam 2, Last regular day of class || Exam 2
 
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|16 || 4/24 || Final class, extra topics ||Final project (MC3-P3)
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|16 || 4/24 || Final Instruction Days (Tuesday), Help Session on Final Project || Project 6
 
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|17 || 5/1 || Final class, extra topics ||Final project (MC3-P3)
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|17 || 5/1 || Finals week, NO FINAL EXAM ||
 
|}
 
|}
  

Revision as of 15:43, 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

  • Schedule:


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)
6 2/13 Market History, Actors, Order Types Project 2
7 2/20 Order Book, Leverage, Valuation
8 2/27 Technical Analysis, Candlestick Chart Patterns, Time Value of Money, CAPM Project 3
9 3/6 Efficient Market Hypothesis, Fund. Law of APM, Efficient Frontier
10 3/13 Review, Exam 1 Exam 1
11 3/20 Finite Automata, MDP, Value/Policy Iteration Project 4
12 3/27 Q-Learning
13 4/3 Misc Machine Learning / 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).

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