Understanding OMS at Georgia Tech
Are you considering enrolling in the Online Master of Science in Computer Science (OMSCS) program at Georgia Tech? One of the courses you might be interested in is OMS7646: Machine Learning for Trading. This course is designed to provide you with a comprehensive understanding of machine learning concepts and their application in the trading industry. Let’s delve into the details of this course and what it has to offer.
Course Overview
OMS7646: Machine Learning for Trading is a course that focuses on the application of machine learning techniques in the context of trading. It covers a range of topics, including random forests, Q-learning in reinforcement learning, and the technical analysis of stock markets. The course is designed to be accessible to students with a basic understanding of machine learning, but it also challenges them to build their own models using numpy, a Python library for numerical computations.
Course Content
The course content is structured to provide a solid foundation in machine learning and its application in trading. Here’s a breakdown of the key topics covered:
| Week | Topic |
|---|---|
| Week 1-2 | Introduction to Machine Learning and Trading |
| Week 3-4 | Random Forests and Decision Trees |
| Week 5-6 | Reinforcement Learning and Q-Learning |
| Week 7-8 | Technical Analysis of Stock Markets |
| Week 9-10 | Building a Trading Model |
| Week 11-12 | Project and Final Presentations |
Course Projects
One of the highlights of this course is the project component. Students are required to build their own trading model using numpy and apply the concepts they’ve learned throughout the course. This project is an excellent opportunity to apply theoretical knowledge to real-world scenarios and gain hands-on experience in trading.
Instructor and Course Materials
The course is taught by Professor Tucker Balch, who has a fascinating background. After serving in the Air Force and flying F-16s, he pursued a degree in computer science and eventually went on to work in quantitative fund management. Professor Balch has also authored a book called “What Hedge Funds Do,” which provides insights into the work of hedge funds. The course materials include lecture notes, readings, and videos, all of which are available on the course website.
Course Difficulty and Workload
While the course covers advanced topics, it is designed to be accessible to students with a basic understanding of machine learning. The workload is manageable, with the course workload decreasing significantly towards the end of the semester. However, the project component can be challenging, as it requires students to apply their knowledge to build a functioning trading model.
Student Feedback
Students who have taken this course have mixed reviews. Some appreciate the course’s manageable workload and the opportunity to learn about trading and machine learning. However, others feel that the course is too basic and that the project component is not challenging enough. Here’s a summary of the feedback:
- Positive aspects:
- Accessible to students with a basic understanding of machine learning
- Opportunity to learn about trading and machine learning
- Manageable workload
- Negative aspects:
- Too basic for students with advanced machine learning knowledge
- Project component not challenging enough
Conclusion
OMS7646: Machine Learning for Trading is a course that offers a unique opportunity to learn about the intersection of machine learning and trading. While the course may not be suitable for everyone, it can be a valuable addition to your OMSCS program if you’re interested in this field. Be sure to consider the course’s content, workload, and your own learning goals before enrolling.