Andreas S. Weigend, Ph.D.

My courses at Stanford, UC Berkeley, and Tsinghua combine real-world case-studies with academic rigor. In my classes, I combine anecdotes, guest speakers, and exercises using actual data sets with concrete, up-to-date information about data mining, algorithms, and machine learning.

My knowledge of recent developments is built on a foundation of 15 years of teaching and more than 100 scientific papers I have published, mainly on applications of machine learning and data mining in finance, business, and the web. My full-time teaching career started as an assistant professor in Computer Science and Cognitive Science at the University of Colorado at Boulder, and continued as an associate professor at the Stern School of Business at New York University (NYU).

While serving as Amazon.com’s first chief scientist, I was approached by the Statistics Department at Stanford in 2003 to develop a course on data mining and e-business. In 2008, Berkeley asked me to create a course reflecting the impact of consumer participation on traditional marketing paradigms. I also taught and continue to teach in top executive MBA programs in Europe and China.

Data can be collected about consumer behavior on the web: on e-commerce sites, in social networks, on dating sites, on mobile phones and so on. This annual course focuses on applying data mining techniques to build predictive models of behavior, create (and reject) hypotheses, design intriguing and fun experiments, and learn from them quickly.

The first half of the course focuses on data: what can be collected, and why it is useful, queries and social search, tags, and interaction data such as email headers. The second half of the course discusses applications, ranging from personalization, recommendations and online marketing (behavioral and situational targeting), to the principles behind collective intelligence, reputation systems, and peer-production.

Students are expected to actively engage in class discussions, to have their assumptions challenged, and to bring their diverse backgrounds to bear.

Course page | Spring 2008 course wiki | Students | Course Evaluations

This newly developed course explores the possibilities for customer-centric marketing in the era of Web 2.0.

In Web 1.0, companies collected data to cut costs and help optimize business processes. In Web 2.0, users are contributing a wide variety of both quantitative and qualitative data, including intentions, attention gestures, geolocation, social relationships, and more. Companies now have profound opportunities to create new technologies to support innovative services. Social recommendations and behavioral targeting are examples of recent uses of these forms of data. What are the implications for old and new business models, products and services? What are our insights and intuitions about what will work in practice? And what are the risks involved?

Course page | Apr 2008 course wiki | Students | Course Evaluations

This course discusses the impact of the communication and data revolution on individuals, business, and society. Companies now have the potential to create unprecedented internal transparency and value for their customers. Applications range from personalization, recommendations, and online marketing to collective intelligence, peer-production, and enterprise 2.0.

Specific topics covered in 2008 include:

• Enablers of the Digital Networked Economy
• Quantifying the business: Creating transparency
• Pricing and Scarcity in the Digital Networked Economy
• Feedback Marketing: Truly engaging users

Students will participate in class discussion and group exercises, and write a “reflection” paper.

I gave my first lecture in China in 1994, and have had a residence in Shanghai since 2000. Since my first courses at Peking University and at Fudan in 1994, I have taught thousands of executive at China’s top schools, including Tsinghua University, Shanghai Jiao Tong University, Cheung Kong Graduate School of Business, and CEIBS (China Europe International Business School).