Artificial Intelligence for Finance
The course aims at helping students to understand Fintech and solve practical AI problems they may encounter in field of finance. The course is designed for practitioners working at financial institutions such as banks, asset management firms or hedge funds, individuals interested in applications of AI for personal trading/portfolio management and current students pursuing a degree in Finance, Statistics, Computer Science, Mathematics or other related disciplines who want to learn practical applications of AI in Finance.
A learner with some or no previous knowledge of AI/Machine Learning (ML) will get to know main algorithms of Supervised and Unsupervised Learning and including different sources of data. We will use AI open source Python/TensorFlow packages to design, test, and implement different AI algorithms in Finance. The theory, concepts, and codebase covered in this course will be extremely useful at every step of the model development life cycle, from idea generation to model implementation.
- Understand different financial data sources
- Learn how to map the practical problem to available AI methods
- Understand which particular AI model would be most appropriate for resolving the problem
- Acquire ability to successfully implement a solution, and assess its performance
Course Typically Offered: Online in Winter and Summer quarters.
Next Step: After completing this course, consider taking additional coursework in the Machine Learning Methods or Selected Topics in Artificial Intelligence certificate programs to continue learning.
Contact: For more information about this course, please email firstname.lastname@example.org.
Note: There is a $25 fee to access the AX Account for this course (access to execute scripts in a persistent environment).
Course Number: CSE-41349
Credit: 3.00 unit(s)
Related Certificate Programs: Selected Topics in Artificial Intelligence
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6/27/2023 - 8/26/2023
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Bilyana Aleksic is senior staff hardware engineer with more than 20 years of experience in complex chip SOC development. She received her master's in control systems from Faculty of Electrical Engineering University of Belgrade, former Yugoslavia, and was an honored student of Mathematical Gymnasium, winning many world Mathematical Olympiad Competitions. Aleksic spent 15 years at Canadian tech company ATI Technologies where she worked on implementation of graphics CPU chips. After she moved to California she spent 7 years in mobile chips implementation and verification methodology development. During the past year she shifted her focus to machine learning for medical applications.
No information available at this time.
No refunds after: 7/3/2023.
6/27/2023 - 8/26/2023
You will have access to your course materials on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
There are no sections of this course currently scheduled. Please contact the Science & Technology department at 858-534-3229 or email@example.com for information about when this course will be offered again.