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 Tuesday Class Date Topic Due
1 1/9 Course Overview, Intro to Markets, Intro to Machine Learning, Pandas Tutorial
2 1/16 Visualizing Market Data, Pricing Stocks, Numpy Tutorial, Working with Time Series
3 1/23 Incomplete Data, Plots, ML Lexicon/Taxonomy, Evaluating Learners MC1-P1
4 1/30 Supervised Learning (KNN, LinReg, Decision Trees, Boosting, Bagging)
5 2/6 Market History, Actors, Order Book, Order Types MC3-P1 + MC3-HW1
6 2/13 Markets, Valuation, Capitalization, Time Value of Money Exam 1
7 2/20 Options, Leverage MC2-P1
8 2/27 Technical Analysis, Candlestick Chart Patterns, CAPM, Efficient Market Hypothesis
9 3/6 Fund. Law of APM, Efficient Frontier, Finite Automata, Markov Decision Processes MC3-P2
10 3/13 Value Iteration, Q-Learning Exam 2
11 3/20 Final class, extra topics Final project (MC3-P3)
12 3/27 Final class, extra topics Final project (MC3-P3)
13 4/3 Final class, extra topics Final project (MC3-P3)
14 4/10 Final class, extra topics Final project (MC3-P3)
15 4/17 Final class, extra topics Final project (MC3-P3)
16 4/24 Final class, extra topics Final project (MC3-P3)
17 5/1 Final class, extra topics Final project (MC3-P3)

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).