Here, we take a closer look at the eight new courses added to MIT Sloan's curriculum this year and why the topics matter to business leaders.
advertising and promotion
The field of advertising and promotion is constantly evolving, and success requires an understanding of how all elements of the marketing mix work together to create an integrated communications program.
MIT Sloan's assistant professor of marketing designed advertisements and promotions to introduce students to this dynamic landscape, with an emphasis on developing integrated marketing communications strategies.
“Understanding integrated marketing communications helps business leaders unite teams around a clear brand vision and reach consumers across different channels,” said Nam. “It's equally important to understand how your audience perceives and responds to these messages, and to ensure that your brand's intent is aligned with your customers' interpretations.”
This course examines how organizations structure their advertising and promotional efforts and explores the intersection of consumer behavior, communication theory, goal setting, and budgeting within the broader marketing framework.
Research on AI and machine learning in finance
MIT Sloan Professor of Finance and This course was designed for finance master's students who are ready to work at the forefront of AI and machine learning applications.
“To remain competitive in the AI era, today's financial professionals must not only effectively use cutting-edge AI tools, but also be able to adapt and enhance them through domain-specific expertise,” said Chen.
This course aims to both expose students to cutting-edge research on AI and machine learning applications in finance and teach them how to conduct rigorous research. In addition, guest speakers from industry will share practical perspectives on how research and development is applied in real-world financial settings.
“The goal is to help students develop rigorous research skills so that they can effectively utilize AI to push the boundaries of quantitative finance,” Chen said.
AI and money
Artificial intelligence is reshaping how financial markets operate, how risks are assessed, and how capital flows through the global economy. How can companies maintain a competitive advantage when the two most dynamic systems in business, AI and finance, are both evolving rapidly?
The Sloan Professor at MIT and former head of the Securities and Exchange Commission designed AI and Money to help students develop the critical reasoning skills needed to seize commercial opportunities and remain relevant at the intersection of these two dynamic fields.
This course examines how machine learning, generative AI, and advanced analytics are redefining asset management, trading, underwriting, customer care, treasury functions, and compliance.
Students explore real-world commercial implications, including AI supply chain decision-making, AI technology stacks, data center economics, geopolitical AI competition, and regulatory frameworks taking shape globally.
financial arrhythmia
Financial markets, like the human heart that experiences arrhythmia, exhibit unique irregular heartbeats that waver, stop, and sometimes beat in ways that defy traditional models.
A Distinguished Senior Fellow at the MIT Golub Center for Finance and Policy, he designed Financial Arrhythmia to help students develop the analytical skills needed to think clearly about the value of financial assets and the underlying causes of asset price fluctuations.
“My life in finance has taught me that the big mistakes we make are often more conceptual than computational: we had the wrong idea, the wrong assumption, or the wrong reference point,” he said.
This curriculum is structured around five key issues that get to the heart of financial decision-making.
- Distinguish between probability and uncertainty.
- Understand risk as distinct from volatility.
- Analyzing intrinsic value through double entry accounting.
- Identify volatility discrepancies within your balance sheet.
- Recognize the limits of understanding when making decisions under conditions of uncertainty.
Deep learning and generative AI in operations research
Deep learning is the engine behind today's most breakthrough AI systems, from chatbots that have subtle conversations to medical diagnostic tools that can detect disease with astonishing accuracy.
“It's no longer necessary for students and business leaders to understand deep learning and generative AI,” said Giorgos Stamou, an MIT Sloan visiting professor who co-teached the class. Associate Dean for Online Education and Artificial Intelligence at MIT Sloan. “AI technology is essential to making informed decisions about how it can drive innovation, improve efficiency, and reshape competitive advantage.”
This class covers both the theory and practice of deep learning, covering architectures such as convolutional networks, transformers, and graph neural networks. Additionally, the fundamentals and frontiers of generative AI are explored, including generative adversarial networks, diffusion models, large-scale language models, and multimodal AI systems.
Students learn technical insights alongside practical applications and ethical and social challenges posed by these powerful technologies. Hands-on work with Python gives participants the opportunity to apply concepts to real-world problems.
Intensive hands-on deep learning
Deep learning powers the AI applications that are transforming today's industries, from language models that can create strategic documents to vision systems that can analyze medical images.
But for many business leaders and aspiring technologists, there remains a frustratingly large gap between understanding the potential of AI and actually building functional models.
Sloan, an operations management professor at MIT, aims to fill that gap by helping students build, train, and deploy models that can solve complex problems with unstructured data (real-world information that doesn't fit neatly into a spreadsheet).
After a brief introduction to deep neural networks and their training, this course moves on to two important areas. One is a language model that focuses on masked generative modeling, and the other is a vision model that uses diffusion and transformers. Students will learn model adaptation techniques such as fine-tuning and reinforcement learning-based training methods.
The curriculum also covers system design concepts essential to production environments, such as search extension generation and agent system design.
Modeling with Machine Learning: Financial Technology
Machine learning has evolved from an experimental tool in finance to a fundamental capability shaping the way the industry operates. Today's financial leaders need to understand not just whether to deploy machine learning tools, but how to strategically apply them across functions such as credit analysis, portfolio management, and risk assessment.
MIT Sloan Professor of Finance, Director of the MIT Sloan Institute for Financial Engineering, Senior Lecturers in Finance designed Modeling with Machine Learning: Financial Technology to fill this gap. This course introduces financial models that balance risk and reward, combined with machine learning tools that can discover and analyze financial patterns that traditional approaches may miss.
Applications span the entire spectrum of modern finance, including valuations, credit analysis, proprietary trading and hedge fund strategies, portfolio management, market structure, risk management and stress testing, natural language processing, and personal finance.
social theory
How can we explain a social order that goes beyond coercion and competitive market prices? Solidarity, status, network ties, identity, and culture deeply shape outcomes, not only explaining oppression and inequality but also defining visions of freedom and successful collaboration.
“The informal and social constraints we face are often less visible and predictable than market prices and formal authorities,” he said. MIT Sloan Associate Professor (Work and Organization Studies). “But they can be just as important for the outcomes we care about.”
Wilmers designed his social theory to provide an overview of social dynamics, drawing primarily from sociology and supplementing lessons from political economy, history, and anthropology.
The first half of the class explores micro-level dynamics such as social connections and embeddedness, groups and positive externalities, groups and closure processes, status and lifestyles, and identity.
In the second half, we consider macro-level forces such as hegemony and coercion, cognitive structures and culture, imagined communities, and politics and economics.
Learn from MIT experts
Some of MIT Sloan's executive education courses taught by the faculty listed above:
applied business analysis
Artificial intelligence: impact on business strategy
Artificial intelligence in medicine
Artificial intelligence in pharmaceuticals and biotechnology
Developing cutting-edge operational strategies
Fundamentals of Finance for Technology Executives
Machine learning in business
Navigating AI: Driving business impact and developing human capabilities
Unsupervised machine learning: Unlocking the potential of your data
