Difference between revisions of "CS7646 Summer 2017 ATL"
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|2 || Visualizing Market Data, Pricing Stocks, Numpy Tutorial, Working with Time Series || | |2 || Visualizing Market Data, Pricing Stocks, Numpy Tutorial, Working with Time Series || | ||
|- | |- | ||
| − | |3 || Incomplete Data, Plots, ML Lexicon/Taxonomy, Evaluating Learners || | + | |3 || Incomplete Data, Plots, ML Lexicon/Taxonomy, Evaluating Learners ||MC1-P1 |
|- | |- | ||
|4 || Supervised Learning (KNN, LinReg, Decision Trees, Boosting, Bagging) || | |4 || Supervised Learning (KNN, LinReg, Decision Trees, Boosting, Bagging) || | ||
|- | |- | ||
| − | |5 || Market History, Actors, Order Book, Order Types || | + | |5 || Market History, Actors, Order Book, Order Types ||MC3-P1 + MC3-HW1 |
|- | |- | ||
| − | |6 || Markets, Valuation, Capitalization, Time Value of Money || | + | |6 || Markets, Valuation, Capitalization, Time Value of Money ||Exam 1 |
|- | |- | ||
| − | |7 || Options, Leverage || | + | |7 || Options, Leverage ||MC2-P1 |
|- | |- | ||
|8 || Technical Analysis, Candlestick Chart Patterns, CAPM, Efficient Market Hypothesis || | |8 || Technical Analysis, Candlestick Chart Patterns, CAPM, Efficient Market Hypothesis || | ||
|- | |- | ||
| − | |9 || Fund. Law of APM, Efficient Frontier, Finite Automata, Markov Decision Processes || | + | |9 || Fund. Law of APM, Efficient Frontier, Finite Automata, Markov Decision Processes ||MC3-P2 |
|- | |- | ||
| − | |10 || Value Iteration, Q-Learning || | + | |10 || Value Iteration, Q-Learning || Exam 2 |
| + | |- | ||
| + | |11 || Final class, extra topics ||Final project (MC3-P3) | ||
|} | |} | ||
| − | + | ==Assignments & Grading== | |
| − | ==Assignments== | ||
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. | 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== | ==Office hours== | ||
| − | David Byrd (Instructor), <B> | + | David Byrd (Instructor), <B>MW 2:20-3:30, Klaus 1456</B>. There is no class after ours in the classroom, so I will simply stay there for up to an hour every class, as long as anyone wants to talk to me! |
| + | |||
| + | Brian Hrolenok, <b>ThF 4:00-5:00pm, CCB 1F Commons</b>. | ||
| + | |||
| + | Nitin Kodialbail, <b>Tu 3:00-5:00pm, CCB 1F Commons</b>. | ||
| − | + | Office hours are also posted on Piazza, which may be updated more frequently than this page: | |
| + | https://piazza.com/class/j2ku8wm3j6o1p9?cid=83 | ||
Latest revision as of 12:52, 8 June 2017
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 | MC1-P1 |
| 4 | Supervised Learning (KNN, LinReg, Decision Trees, Boosting, Bagging) | |
| 5 | Market History, Actors, Order Book, Order Types | MC3-P1 + MC3-HW1 |
| 6 | Markets, Valuation, Capitalization, Time Value of Money | Exam 1 |
| 7 | Options, Leverage | MC2-P1 |
| 8 | Technical Analysis, Candlestick Chart Patterns, CAPM, Efficient Market Hypothesis | |
| 9 | Fund. Law of APM, Efficient Frontier, Finite Automata, Markov Decision Processes | MC3-P2 |
| 10 | Value Iteration, Q-Learning | Exam 2 |
| 11 | Final class, extra topics | Final project (MC3-P3) |
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), MW 2:20-3:30, Klaus 1456. There is no class after ours in the classroom, so I will simply stay there for up to an hour every class, as long as anyone wants to talk to me!
Brian Hrolenok, ThF 4:00-5:00pm, CCB 1F Commons.
Nitin Kodialbail, Tu 3:00-5:00pm, CCB 1F Commons.
Office hours are also posted on Piazza, which may be updated more frequently than this page: https://piazza.com/class/j2ku8wm3j6o1p9?cid=83
