Fall 2022

The goal of the class is to learn the basics of probability theory, how to reason rigorously about probability, to learn concentration inequalities (Markov, Chebyshev, Chernoff, ++) and know to apply those in the context of algorithm analysis and design.

Textbook: Randomized Algorithms by Mitzenmaher and Upfal (Second Edition). We will cover approximately the 7 first chapters, plus possibly additional readings. The precise syllabus will develop as we go along! See Q&A Lecture Schedule post for the approximate schedule. 

In this course, we will examine upcoming user interface technologies that will impact how we interact with our devices and digital content in the future. These include: immersive technologies (augmented and virtual reality), physiological interfaces (e.g., brain and body interfaces), wearable computing (e.g., devices both for reading and writing data to the user's body), multisensory and multimodal interactions in mixed, augmented and virtual realities (e.g., spatial audio, body movement), haptics (e.g., force feedback, sensing weight, feeling textures), and others.

This course explores advanced topics in highly scalable Internet services and their underlying systems’ architecture. Software today is primarily delivered as a service: accessible globally via web browsers and mobile applications, and backed by millions of servers. Modern web frameworks (e.g., Ruby on Rails, Django, and Express), and continuous improvements to cloud providers (e.g., Amazon Web Services, Google Cloud Platform, and Microsoft Azure) make it increasingly easier to build and deploy these systems.

This course covers advanced topics on information retrieval, web search, and related scalable information systems. The topics include search engines, indexing, retrieval and ranking, and support for large-scale online search services/data processing. Recent papers in top conferences will be reviewed, and issues in relevance, efficiency, scalability, and possibly privacy will be studied.

Machine Learning is about developing systems that automatically improve their performance through experience. It has found massive applications in real products. Examples include systems that recommend online videos, automatic translating languages, and autonomous driving vehicles. This course covers the theory, models, and practical algorithms for machine learning from a variety of perspectives.