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Kogod Students Get Ahead with Machine Learning

Kogod School of Business professor of finance Ali Sanati discusses his new course in machine learning and how students can keep up with advancements in AI.


It’s a fascinating time to learn about artificial intelligence at the Kogod School of Business. With the recent unveiling of Kogod’s unprecedented framework for incorporating AI throughout its curriculum, students have new and ample opportunities to learn about this evolving technology and enter their careers with a better understanding of it.


However, these new courses, certificates, and training offerings won’t be Kogod’s first foray into this technology. This semester, finance professor Ali Sanati has been teaching a course designed to introduce students to how machine learning can best be utilized in financial contexts. Course FIN666, “Advanced Quant Methods and Machine Learning in Finance,” covers machine learning techniques in investments, financial forecasting, and beyond, focusing on practical applications and hands-on experience. As the semester winds down, we talked with Professor Sanati to learn more about the course, what Kogod students should expect when they sign up for it, and why an understanding of machine learning can put them ahead when applying for jobs.

Kogod: Can you introduce us to the fundamentals of FIN666 and what Kogod students can expect to learn?

Sanati: FIN666 is a graduate-level course designed to equip students with the latest quantitative methods and machine learning techniques applied in the finance sector. Throughout the course, students explore topics such as investments and asset allocation, time series analysis and forecasting, machine learning applications in finance, and causal inference methods.

A distinguishing feature of this course is its emphasis on practical implementation.”

Ali Sanati AI

Ali Sanati

Professor of Finance, Kogod School of Business

Students gain a theoretical understanding of these methods and acquire hands-on experience by applying these techniques to real-world finance problems using Python. This approach ensures that by the end of the course, students are proficient in analyzing financial data, building predictive models for various financial applications such as fraud detection, loan and insurance underwriting, and asset return predictions, and can make informed investment decisions.

What was the process of developing this course? With AI and machine learning evolving rapidly, how did you determine what to include?

I developed this course from scratch three years ago, drawing upon a diverse range of eight textbooks and handbook chapters, focusing on the practical application of theoretical concepts. This led to the creation of over three hundred pages of lecture notes, computational programs, and exercises that cover a comprehensive set of topics.

The objective guided the decision on what to include in the curriculum to offer content that reflects up-to-date and popular quantitative methods in the finance sector, balanced with the need for practical implementation. I keep the course updated as new methods and models are developed. For example, this year is the first time I’m teaching my students about the foundations of large language models and generative AI, as well as their applications in finance.

What skills and takeaways do you hope students in this course will take with them?

After completing the course, students are expected to have a robust set of highly valuable skills in today’s job market—proficiency in Python, an in-depth understanding of machine learning models, and the ability to apply quantitative methods to solve complex financial problems. My goal has been to develop students’ analytical thinking and problem-solving abilities as they learn the theory and applications of various models and techniques. These skills are not only critical in navigating the complexities of the finance industry but also useful in promoting innovation and strategic decision-making in their future careers.

What feedback have you gotten from students about the course so far?

Feedback from students shows that this class has been among the most challenging and rewarding courses within their programs. I perceive that students appreciate the balance between theoretical knowledge and practical application, especially the hands-on experience gained through our lab sessions and in-class exercises.

The course’s structure promotes collaborative learning and engagement, with many students highlighting the value of discussing assignments and comparing results with their peers.”

Ali Sanati AI

Ali Sanati

Professor of Finance, Kogod School of Business

Many of my students in previous years have leveraged their knowledge of Python programming, machine learning, and other similar techniques learned in this class to get job interviews and attractive offers in roles that directly apply those skills. Hearing this type of feedback from my students is one of the most rewarding aspects of designing and offering a course like this.

How can an understanding of AI and machine learning benefit finance students—and Kogod students in general—as they enter the workforce?

Understanding AI and machine learning offers Kogod students a significant edge as they enter the workforce. These technologies are transforming the finance industry—and many other areas—by enabling more accurate predictions, financial forecasting, and risk assessment. For finance students, proficiency in these areas means innovating and leveraging data-driven insights for decision-making. More broadly, Kogod students proficient in AI and machine learning capabilities are better positioned to navigate digital transformation across industries, making them highly attractive to employers looking for candidates who can blend traditional business knowledge with cutting-edge analytical skills.

Do you have other ideas for incorporating AI and machine learning education into your courses in the future?

Looking ahead, the goal is to continue evolving the course content to keep pace with the rapid advancements in AI and machine learning. This includes integrating the latest research findings, tools, and case studies to ensure students learn state-of-the-art applications.

Learn more about Kogod's incorporation of AI here.