CS4646 Spring 2018

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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).