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New MIT Sloan courses focus on deep learning, generative AI, and financial technology
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Here are details on eight new courses added to MIT Sloan’s curriculum this year — and reflections on why their topics matter to business leaders.
Advertising and Promotions
The field of advertising and promotions is constantly evolving, and success requires understanding how all of the elements of the marketing mix work together to create an integrated communications program.
assistant professor of marketing at MIT Sloan, designed Advertising and Promotions to introduce students to this dynamic landscape, with a focus on developing integrated marketing communications strategies.
“Understanding integrated marketing communications helps business leaders unify their teams behind a clear brand vision and reach consumers across various channels,” Nam said. “It’s just as crucial to understand how audiences perceive and react to these messages, ensuring that brand intentions align with customer interpretations.”
The course examines how organizations structure their advertising and promotional efforts and explores the intersection of consumer behavior, communications theory, goal setting, and budget management within the broader marketing framework.
AI and Machine Learning Research in Finance
MIT Sloan finance professors and designed this course for Master of Finance students ready to work at the frontier of AI and machine learning applications.
“To maintain a competitive edge in the age of AI, today’s finance experts not only need to work effectively with cutting-edge AI tools but also be able to adapt and enhance them through domain-specific expertise,” Chen said.
The course aims to both expose students to state-of-the-art 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 developments are being applied in real-world financial settings.
“The goal is to help students develop rigorous research skills so that they can effectively use AI to push the boundaries of quantitative finance,” Chen said.
AI and Money
Artificial intelligence is reshaping how financial markets operate, how risk is assessed, and how capital flows through the global economy. How do businesses maintain competitive advantage when two of the most dynamic systems in business — AI and finance — are both evolving rapidly?
MIT Sloan professor of the practice and former chair of the Securities and Exchange Commission, designed AI and Money to help students develop the critical reasoning skills needed to seize commercial opportunities, and maintain relevance, at the intersection of these two dynamic fields.
The course examines how machine learning, generative AI, and advanced analytics are redefining asset management, trading, underwriting, customer interactions, finance functions, and compliance.
Students will explore real-world commercial implications, including AI supply chain decisions, the AI tech stack, data center economics, geopolitical AI competition, and regulatory frameworks taking shape globally.
The Arrhythmia of Finance
Financial markets, like a human heart experiencing cardiac arrhythmia, exhibit their own irregular pulses — lurching, pausing, and sometimes racing in ways that defy conventional models.
a distinguished senior fellow at the MIT Golub Center for Finance and Policy, designed The Arrhythmia of Finance to help students develop the analytic skills needed to think clearly about the value of financial assets and the underlying sources of volatility in asset prices.
“A life in finance has taught me that the big mistakes we make are more often conceptual than computational — we had the wrong ideas, the wrong assumptions, or the wrong reference points,” he said.
The curriculum is organized around five key challenges that get to the heart of financial decision-making:
- Distinguishing probability from uncertainty.
- Understanding risk as distinct from volatility.
- Analyzing intrinsic value through double-entry bookkeeping.
- Identifying volatility mismatches within balance sheets.
- Recognizing the limits of our understanding when making decisions under conditions of uncertainty.
Deep Learning and Generative AI in Operations Research
Deep learning is the engine behind some of today’s most groundbreaking AI systems, from chatbots that can engage in nuanced conversation to medical diagnostic tools that can detect disease with remarkable accuracy.
“For students and business leaders, understanding deep learning and generative AI is no longer optional,” said MIT Sloan visiting professor Giorgos Stamou, who co-teaches the class with associate dean for online education and artificial intelligence at MIT Sloan. “It’s essential for making informed decisions about how AI technologies can lead to innovation, improve efficiency, and reshape competitive advantage.”
In this class, both the theory and practice of deep learning are described, covering architectures such as convolutional networks, transformers, and graph neural networks. Moreover, the foundations and frontiers of generative AI are explored, including generative adversarial networks, diffusion models, large language models, and multimodal AI systems.
Students study technical insights alongside practical applications and the ethical and societal challenges posed by these powerful technologies. Hands-on work in Python gives participants the opportunity to apply concepts to real-world problems.
Intensive Hands-On Deep Learning
Deep learning powers the AI applications transforming industries today — from language models that can draft strategy documents to vision systems that can analyze medical images.
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Yet for many business leaders and aspiring technologists, the gap between understanding AI’s potential and actually building functional models remains frustratingly wide.
MIT Sloan professor of operations management, aims to bridge that gap by helping students build, train, and deploy models that can solve complex problems involving unstructured data — the messy, real-world information that doesn’t fit neatly into spreadsheets.
After a rapid introduction to deep neural networks and their training, the course dives into two critical areas: language models, focusing on masked and generative modeling, and vision models that use diffusions and transformers. Students will learn model adaptation techniques, including fine-tuning and reinforcement-learning-based training methods.
The curriculum also covers system design concepts essential for production environments, including retrieval augmented generation and agentic system design.
Modeling With Machine Learning: Financial Technology
Machine learning has evolved from an experimental tool in finance to a fundamental capability shaping how the industry operates. Today’s financial leaders need to understand not just whether to deploy machine learning tools but how to apply them strategically across functions like credit analysis, portfolio management, and risk assessment.
MIT Sloan professor of finance and director of the Laboratory for Financial Engineering at MIT Sloan, and senior lecturer in finance, designed Modeling With Machine Learning: Financial Technology to bridge this gap. The course introduces financial models that balance risk and reward, paired with machine learning tools that can uncover and analyze financial patterns that traditional approaches might miss.
Applications span the full spectrum of modern finance: valuation, 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 do we explain social order beyond coercion and competitive market prices? Solidarity, status, network ties, identity, and culture shape outcomes in profound ways — explaining oppression and inequality but also defining visions of freedom and successful collaboration.
“The informal, social constraints we face are often harder to see and less predictable than market prices or formal authority,” said MIT Sloan associate professor of work and organization studies. “But they can be just as important for the outcomes we care about.”
Wilmers designed Social Theory to provide an overview of social dynamics, drawing mainly from sociology and supplemented with lessons from political economy, history, and anthropology.
The first half of the class explores micro-level dynamics: social ties and embeddedness, groups and positive externalities, groups and closure processes, status and styles of life, and identity.
The second half examines macro-level forces: hegemony and coercion, cognitive structures and culture, imagined communities, and politics and the economy.
LEARN FROM THE MIT EXPERTS
A selection of MIT Sloan executive education courses taught by faculty members mentioned above:
Artificial Intelligence: Implications for Business Strategy
Artificial Intelligence in Health Care
Artificial Intelligence in Pharma and Biotech
Developing a Leading Edge Operations Strategy
Fundamentals of Finance for the Technical Executive
Navigating AI: Driving Business Impact and Developing Human Capability
Unsupervised Machine Learning: Unlocking the Potential of Data
