天美传媒

Autumn 2025

43120, Building an AI Company - Carlos Ganoza

AI is transforming industries at an unprecedented pace, creating new opportunities for startups and established businesses alike. It represents a fundamental shift in how businesses operate. Yet, while AI’s potential is widely discussed, far less attention is given to how to actually build and scale AI products and companies. AI products have different dynamics from traditional software -from the cold start problem and high marginal costs to ethical risks and usability constraints. Successfully navigating these challenges requires a different strategic approach and operational playbook. This course fills that gap.

Designed for future founders, executives and investors, the course aims to provide students with a deep and practical understanding of what it takes to create and grow an AI-driven business. Students will learn about key principles and tradeoffs when developing effective product and monetization strategies, the ethical and technical considerations that have to go into AI business decisions, and lessons from early failures and successes of companies that have scaled.

The course is built around two core themes: achieving product-market fit in AI, and the challenges AI companies face when trying to build, scale and sustain competitive advantage.

37105, Data Science for Marketing Decision Making - Giovanni Compiani

Marketing decisions in the era of big data and artificial intelligence (AI) are based on a statistical analysis of large amounts of transaction and customer data. Using such an analysis we can predict the profitability or ROI of different marketing decisions, such as pricing, customer targeting, or digital advertising.

The goal of this class is to introduce modern data-driven marketing techniques and train the students as data scientists who can analyze data and make marketing decisions using state-of-the-art tools employed in the industry. We will cover a wide range of topics, including demand modeling, the analysis of household-level data, customer relationship management (CRM), and digital marketing.

The focus is on predicting the impact of marketing decisions, including pricing, advertising, and customer targeting, on customer profitability and the ROI from a customer interaction. The students will get immersed in a workflow that begins with the initial processing of the raw data and ends with the implementation of the marketing decision. First, we will learn how to manage and process large databases. The key tools used include some key packages in R that are designed for big data processing. Second, we will discuss and apply modern statistical tools building on regression analysis, including some key tools from the machine learning literature. Finally, we will learn how to implement key marketing decisions based on the statistical analysis of the data.

Note: The broad set of topics in this class overlaps with the topics covered in 37103 (Data-Driven Marketing). However, we will cover these topics at a faster pace and emphasize state-of-the-art techniques that are only briefly surveyed or not covered in 37103. Also, the main goal of the data assignments in 37103 is to make the students familiar with some key concepts in data-driven marketing. This class goes above and beyond this goal and introduces the students to a professional data scientist’s workflow used for marketing decision-making. 

41215, Data Intelligence - Veronika Rockova

This course sits at the intersection of Data Science and Artificial Intelligence. Designed for students preparing for careers in data-driven environments, the course emphasizes practical concepts and tools commonly used by data scientists in business contexts. Rather than
focusing on coding, the course prioritizes data storytelling–the ability to interpret, analyze, and communicate insights from data.

Each lecture features the analysis of two to three real-world datasets, demonstrated live in class. Examples include consumer database mining, internet and social media tracking, asset pricing,
network analysis, sports analytics, and text mining.

The curriculum spans topics from classical statistics (e.g. hypothesis-driven decisions), data science (dimensionality reduction) to modern machine learning techniques (e.g deep learning). It
also explores cutting-edge advancements in generative AI. The course puts a particular emphasis on the analysis of text data in the context of both small and Large Language Models (LLM) that
form a basis of popular text-generating systems. Techniques covered include large-scale testing and false discovery rates, modern regression and model choice, machine-learning based classification,
network analysis, language and topic models, principal components, clustering, Bayesian analysis, deep learning, transformers and attention.

By the end of the course, students will be equipped to perform machine-supported intelligent data analysis and communicate findings effectively.

41916, Bayes, AI, and Deep Learning - Nicholas Polson

This course focuses on the applications of data analytic, machine learning and deep learning methods. We will start with a quick review of basic Bayesian models followed by tools and concepts from artificial intelligence. Students will learn how to use deep learning to analyze a variety of complex real world problems. Numerous empirical examples from finance, internet analytics, and sports are used to illustrate the material covered. Google’s development of deep neural networks and applications will be discussed in detail.

Emphasis will be placed on understanding concepts of Bayes, AI and Deep Learning. The three main topics covered are: (i) Bayesian methods including conditional probability,hierarchical models (ii) Artificial Intelligence including modern regression methods such as lasso and ridge regression. Dimensionality reduction techniques and sparsity are central to data analysis (ii) Deep Learning including Neural Nets, Architecture design,Stochastic Gradient Descent, speeding up convergence. Throughout business and internet applications including machine intelligence, reinforcement learning, image and speech recognition will be used to illustrate the wide range of applications.

Winter 2026

32210, Generative Thinking - Sanjog Misra

Generative AI is a pivotal advancement in the realm of artificial intelligence that has the potential to transform various industries, from entertainment and advertising to healthcare and education. A thorough
understanding of Generative technologies, potential uses cases and the implications are going to be necessary conditions of success. This new technology can automate content creation, optimize processes,
and personalize experiences but more broadly it has the capacity to synthesize new data, simulate scenarios, generate ideas, and even design novel solutions to complex problems., making it a crucial
technology for managers across all domains.

This course provides a comprehensive overview of Generative AI, covering its foundational technologies, practical applications, and broader societal implications. It aims to equip students with a solid
understanding of Generative AI and its relevance to various fields, as well as cultivate critical thinking on the ethical, legal, and societal challenges related to its deployment.

Each class will have a lecture and discussion portion as well as some practicum/exercise/demo portion. In addition, there will be a course projects where you will work on the development of a Generative AI solution.

43100, AI Essentials - Dacheng Xiu

This course is designed to introduce students to the cutting-edge field of Artificial Intelligence (AI). Across detailed lectures, participants will gain insights into the core principles of AI and machine learning, explore the intricacies of natural language processing, discover the potential of vision recognition and image generation technologies, delve into reinforcement learning for sequential decision-making, and learn about the design of recommender systems that power personalized user experiences. The course also examines the burgeoning field of generative AI and the AI-generated content industry, while emphasizing the crucial importance of bias assessment and the principles of responsible AI.

This course is designed for those who are interested in the inner workings of state-of-the-art AI technologies. Specifically, it places emphases on the philosophy and intuition behind these technologies, as well as their promises and perils, but not on the technical details.

43130, AI for Good - Carlos Ganoza

AI is transforming industries, societies, and daily life—offering immense opportunities for innovation but also posing significant challenges. How can business and product leaders ensure AI is designed and deployed in ways that maximize positive impact on users and society?

This course provides a practical, decision-making framework for building AI products that are responsible and maximize human wellbeing. Through discussions of relevant research, case studies, and hands-on exercises, students will learn how to evaluate when and how AI products can drive social good, mitigate unintended harms, and embed AI responsibility into organizational culture and product development.
Students will apply these concepts by selecting an AI product—either an existing one to be redesigned for greater positive impact or a new project of their own—and refining it throughout the course using the principles learned.

The course will cover both AI products designed explicitly to tackle societal problems (such as healthcare, climate action, financial inclusion, etc.), and commercial AI products that while profit driven can be designed in ways that enhance user well-being.

35137, Machine Learning in Finance - Leland Bybee

Machine Learning in Finance focuses on the use of machine learning and AI methods and their applications in finance with a particular focus on problems in asset pricing.  This course aims to provide students with the knowledge necessary to best use recent machine learning methods but also to understand their limitations.  We will cover such topics as penalized estimation and its use in forecasting, clustering, factor models and unsupervised learning, neural networks and non-linear prediction, text data and large language models.

It is expected that students will have some exposure to programming in Python.  Students will work with real financial datasets to help gain a better understanding of the methods covered.

Spring 2025

43110, Artificial Intelligence - Carlos Ganoza

This course is designed to introduce students to the cutting-edge field of Artificial Intelligence (AI). Across detailed lectures, participants will gain insights into the core principles of AI and machine learning, explore the intricacies of natural language processing, discover the potential of vision recognition and image generation technologies, delve into reinforcement learning for sequential decision-making, and learn about the design of recommender systems that power personalized user experiences. The course also examines the burgeoning field of generative AI and the AI-generated content industry, while emphasizing the crucial importance of bias assessment and the principles of responsible AI.

This course is designed for those who are interested in the inner workings of state-of-the-art AI technologies. Specifically, it places emphases on the philosophy and intuition behind these technologies, as well as their promises and perils, but not on the technical details.

41204, Machine Learning - Christian Hansen

This course offers a broad introduction to key concepts in machine learning, with a focus on applications in the business world. We will cover the three core learning paradigms: (i) supervised learning, (ii) unsupervised learning, and (iii) reinforcement learning. Each paradigm will be illustrated through simple examples designed to highlight essential ideas and practical considerations. In addition to the basic learning methods, we will explore topics - such as causal inference, transfer learning and fine-tuning deep neural networks, and AI agents - as time permits. The goal is for students to leave the course with a solid grasp of core machine learning concepts, the ability to engage in informed discussions, and a clearer sense of both the opportunities and limitations of applying machine learning in business contexts.

The course is introductory and will not dig into the mathematical minutiae of machine learning. It is meant to be accessible to students with an understanding of basic statistical concepts and mathematics. Importantly, the course is not about coding and will provide no formal instruction in programming. However, coursework will involve implementing machine learning methods, so students without a coding background will need to be self-directed and make use of external resources to complete assignments. Students are strongly encouraged to use AI tools to support coding, interpretation, and learning throughout the course.