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Machine Learning for Finance

Bridge finance and technology with practical machine learning expertise.

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Explore finance-applied machine learning with industry insiders.

Today’s businesses need data-based financial analysis to gain deeper insights that will enable them to connect operations to long-term value, model scenarios in real time, and allocate resources efficiently. The increasing demand for advanced finance functions and technological advancements in cloud-based services have led to the financial analytics market’s significant growth.

The University of Chicago’s eight-week Machine Learning for Finance course focuses on collecting, organizing, and using data to perform advanced financial analysis with algorithms and statistical techniques and tools. You will engage with real-world case studies and examples, allowing you to apply the theory you will learn to financial models.

$74 k

The average annual base pay for a financial analyst in the United States.
Source: Glassdoor

$43 b

The anticipated value of the financial analytics market by 2030.
Source: Global Market Insights

15%

The projected CAGR of the financial analytics industry from 2022 to 2030.
Source: Global Market Insights

Course Modules

  • Introduction to Probability

  • Introduction to Statistics

  • Introduction to Python I

  • Introduction to Python II

  • Introduction to Python III

  • Pandas

  • Exploratory Data Analysis

  • Linear Regression

  • OLS Analysis

  • Regression Metrics

  • Bias-Variance Tradeoff

  • Model Testing

  • Model Validation

  • Feature Engineering

  • Regularization

  • Time Series Data

  • Autoregressive Model (AR)

  • Moving Average Model (MA)

  • The ARIMA Model

  • Working with Time Series Data in Python

  • GARCH/ARCH Models

  • Implementation of GARCH/ARCH

  • Analyzing S&P 500 Data

  • Supervised Learning

  • Classification Algorithms: K-Nearest Neighbors

  • Classification Algorithms: Logistic Regression

  • Classification Metrics

  • Unsupervised Learning

  • Ensemble Methods: Bagging, Random Forests

  • Ensemble Methods: Boosting

  • Basics of Risk and Capital Allocation

  • Capital Allocation Decision: One Risky and One Risk-Free Asset

  • The Consumer's Preferences

  • Risk Aversion and Utility Values

  • Expected Return and Variance on a Portfolio of Two Risky Assets

  • Covariance and Correlation

  • A Trip to Monte Carlo

  • Neural Networks: Starting Point

  • Neural Networks: Activation Functions and Vanishing Gradients

  • Big Data

  • Approaches to Big Data: Hadoop

  • Approaches to Big Data: MapReduce and Spark

After completing the course, you will be able to:

  1. Apply statistics and probability concepts to finance.

  2. Understand what exploratory data analysis is and how to perform it with Python and Pandas.

  3. Engineer new features and functions from existing data.

  4. Comprehend how unsupervised machine learning models work and when they can be useful.

  5. Use simulation to solve portfolio risk and allocation problems and answer financial questions.

  6. Pioneer data-driven strategies that enhance financial forecasting, risk assessment, and investment analysis.

This course is an excellent fit for:

  • CFOs and other senior finance leaders eager to drive data-centric financial strategies and lead digital transformation in their organizations.

  • Investors, venture capitalists, and portfolio managers looking to enhance risk assessment and optimize investment strategies using data-driven methodologies.

  • Consultants who want to integrate advanced machine-learning techniques into financial modeling and decision-making.

  • Diverse finance professionals interested in leveraging machine learning and predictive analytics to enhance financial performance and decision-making.

  • Analysts and data professionals looking to transition into finance or specialize in fintech, quantitative analysis, or financial risk modeling.

Past Participant Profiles

Participants in this course bring diverse industry experience, roles, and expertise, fostering a dynamic exchange of ideas. This diversity enriches discussions, leading to deeper insights and innovative approaches to complex financial challenges.

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Diverse Functional Responsibilities

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A Wide Range of Work Experience

Unique Program Features

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Live Sessions
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World-Class Instructors
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Hands-On Learning

UChicago Credentials

By successfully completing the course, you will receive a credential from the University of Chicago, a digital badge, and earn 4.6 Continuing Education Units (CEUs). You will also become part of the UChicago network.

UCH Generic Certificate

Our Approach to Online Learning

UChicago’s Machine Learning for Finance course will teach you to collect, organize, and use data to perform advanced financial analysis with algorithms and statistical techniques.

Flexible

Make the most of your time with a format that lets you study and complete coursework at your own pace. If you are unable to attend webinars or live sessions, you can access them later.

Interactive

Our interactive content includes videos from instructors at the University of Chicago and visual and graphic materials that provide real-life examples.

Personalized

The instructional assistant—a subject matter expert—will accompany you on your journey through the content, answering your questions and providing feedback on your work.

Meet Your Instructor

These instructors teach this course regularly. Please speak to your enrollment advisor if you wish to know who the current teacher is.

UCH Faculty Sourav Ghosh
Sourav Ghosh, MSc

Vice President at an Investment Bank

UCH Faculty Lara Kattan
Lara Kattan, MPP

Clinical Assistant Professor of Operations Management

Student Feedback

This course is valuable for gaining further knowledge of ML content for finance. It enhanced my understanding of application principles and increased my confidence in my ability to make decisions related to problems that can be solved by applying the concepts covered.
Derrick Walker
Technology Process and Reporting, Nike Inc.

Frequently Asked Questions

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Didn't find what you were looking for? Schedule a call with one of our Program Advisors or call us at +1 315 810 9499.

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