Competitive organizations need teams who can leverage massive and complex datasets, deriving insights that are strategic and actionable. In the financial sector, machine learning has emerged as one of the most critical tools for decision-making. Whether it’s streamlining operations, informing investment decisions, or assessing risk, finance professionals with machine learning skills that improve decision making will have a discernible competitive advantage.
In this professional certificate program, you will learn the key skills for constructing machine learning models, and using data to inform decisions. Whether you are a trader, financial analyst or programmer; whether your focus is on portfolio management or quantitative analytics, you will acquire the skills to apply both Classical Machine Learning and Neural Network/Deep Learning solutions to problems in finance. As important: you will learn a systematic approach to problem solving through data analysis, increasing your value in the emerging data-driven world. Combined with both theory and practical advice, you will be well positioned for solving the supervised and unsupervised learning tasks that will be critically important to all organizations.
The urgent demand for machine learning in finance is only going to grow. But the skills you will develop in this program are key to decision making in many other domains as well. Having these skills will enhance your value in many industries and will be invaluable to your career.
Courses under this program: Course 1: Classical Machine Learning for Financial Engineering
Learn a systemic approach to utilizing classical machine learning models and techniques to gain insights from data sets, and master the tools used in this task.
Course 2: Deep Learning and Neural Networks for Financial Engineering
Expand your machine learning toolkit to include deep learning techniques, and learn about their applications within finance.
Deep Learning ventures into territory associated with Artificial Intelligence. This course will demonstrate how neural networks can improve practice in various disciplines, with examples drawn primarily from financial engineering. Students will gain an understanding of deep learning techniques, including how alternate data sources such as images and text can advance practice within finance.
Classical Machine Learning refers to well established techniques by which one makes inferences from data. This course will introduce a systematic approach (the “Recipe for Machine Learning”) and tools with which to accomplish this task. In addition to the typical models and algorithms taught (e.g., Linear and Logistic Regression) this course emphasizes the whole life cycle of the process, from data set acquisition and cleaning to analysis of errors, all in the service of an iterative process for improving inference.
Our belief is that Machine Learning is an experimental process and thus, most learning will be achieved by “doing”. We will jump-start your experimentation: Engineering first, then math. Early lectures will be a "sprint" to get you programming and experimenting. We will subsequently revisit topics on a greater mathematical basis.