CS7646 Summer 2017 ATL

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This summer, the course will follow this broad outline:

  1. Brief introduction to Manipulating Financial Data in Python
  2. Introduction to Machine Learning
  3. Computational Investing
  4. 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!

TAs TBD. A poll was sent out for TA office hours via T-Square. Be sure to complete it!