Difference between revisions of "CS4646 Spring 2018"
Jump to navigation
Jump to search
| Line 64: | Line 64: | ||
* [[http://quantsoftware.gatech.edu/CS4646_assess_learners assess_learners]] 10% (challenging) | * [[http://quantsoftware.gatech.edu/CS4646_assess_learners assess_learners]] 10% (challenging) | ||
* [[http://quantsoftware.gatech.edu/CS4646_marketsim marketsim]] 10% (moderate) | * [[http://quantsoftware.gatech.edu/CS4646_marketsim marketsim]] 10% (moderate) | ||
| − | * | + | * [[http://quantsoftware.gatech.edu/CS4646_manual_strategy manual_strategy]] 10% (moderate) |
* Q-Learning Robot 10% (moderate) | * Q-Learning Robot 10% (moderate) | ||
* Strategy Learner 15% (very challenging) | * Strategy Learner 15% (very challenging) | ||
Revision as of 15:00, 2 March 2018
Undergraduate Syllabus
Note that you are on the syllabus page for the undergraduate on-campus MLT class, CS 4646. This syllabus does not apply to the graduate class, CS 7646.
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
- Forum: https://piazza.com/class/jby8m47v3v52oc (just search for CS 4646 if this doesn't work)
- 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.
- Exams: Exams are on Thursday so I can do some review on Tuesday.
| 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, Drop Day (3/14) | Project 4 |
| 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)
- [assess_learners] 10% (challenging)
- [marketsim] 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).
