CS7646 Summer 2017 ATL
Contents
Overview
This summer, the course will follow this broad outline:
- Brief introduction to Manipulating Financial Data in Python
- Introduction to Machine Learning
- Computational Investing
- Machine Learning Algorithms for Trading
Instructor information
David Byrd
Research Scientist, Interactive Media Technology Center at Georgia Tech
2017 Summer Schedule
Class meets MW 12:30 - 2:20 in Klaus 1456
2017 Summer Schedule
This schedule is tentative and subject to change due to the compressed summer timeline. I am not certain exactly how quickly we will progress through the material.
The first exam will be given in week 6 or 7 depending on our progress. I will nail it down early in the class.
The second exam will be given on the final day of regular class in week 10.
| Week | Topic | Due |
|---|---|---|
| 1 | Course Overview, Intro to Markets, Intro to Machine Learning, Pandas Tutorial | |
| 2 | Visualizing Market Data, Pricing Stocks, Numpy Tutorial, Working with Time Series | |
| 3 | Incomplete Data, Plots, ML Lexicon/Taxonomy, Evaluating Learners | |
| 4 | Supervised Learning (KNN, LinReg, Decision Trees, Boosting, Bagging) | |
| 5 | Market History, Actors, Order Book, Order Types | |
| 6 | Markets, Valuation, Capitalization, Time Value of Money | |
| 7 | Options, Leverage | |
| 8 | Technical Analysis, Candlestick Chart Patterns, CAPM, Efficient Market Hypothesis | |
| 9 | Fund. Law of APM, Efficient Frontier, Finite Automata, Markov Decision Processes | |
| 10 | Value Iteration, Q-Learning |
Assignments & Grading
This is a project-heavy class. There will be 5 projects this semester, due roughly every two weeks. Note that there are two non-cumulative exams during the semester. The final project in the class (and the most complex) will be due during the final exam period, in lieu of a final exam.
Assignments are available on the main course page and are the same for the on-campus and online class.
Participation: For the 3% participation credit, Piazza participation is not necessary for on campus students. Participation will be judged based on attendance and attention at the lectures.
Office hours
David Byrd (Instructor), TBD
TAs TBD
