Machine Learning for Finance
Winter Quarter
Instructor: Niels Nygaard
Syllabus
The course will focus on two Machine Learning categorization models: Logistic Regression and Support Vector Machines, both binary and multi-category. The course will develop the mathematical foundations for these models and the optimization algorithms for training them on actual data. The algorithms will be implemented in Python. The necessary parts of Python programming will be taught along the way as they are needed. The Machine Learning models will be used to train models for trading stocks based on both fundamental and technical data. The models will be implemented in Python, using several Machine Learning libraries such as Scikitlearn, and back-tested using the web service Quantopian. At the end of the course, the students will develop and implement their own trading models and analyze the performance of their models.
Please note that this course is Python-coding intensive. A prerequisite for Machine Learning for Finance is Python: either via FINM 32500 - Computing for Finance in Python or program approval.
This course counts towards the Financial Data Science concentration.