Grad Course: Designing Intelligent Self-Improving Systems Through Human Computation, Randomized A/B Experiments and Statistical Machine Learning

UPDATE: Anyone is welcome to attend the first day of classes. Computer science students can register on ROSI. Students in other departments are welcome to take it and will likely get a spot (room size is 70+), but ROSI will only allow enrollment in September.  

The course will be held on Wednesdays from 2 - 4 PM, in SK 548.

To express interest in taking the course email To sign up for updates on the course content as it becomes available, request to join the google group "Info For Grad Course: Designing Intelligent Self-Improving Systems".

The course will give students an introduction to research on how to design and deploy software systems that can be deployed to real users and use data to automatically improve (see For example, building lessons that continually improve and personalise which explanations are provided to students, or building apps that motivate people to change behaviour by crowdsourcing motivational messages and use machine learning to experiment with which messages change people's decisions. 

Designing these systems draws on human-computer interaction research on crowdsourcing and human computation to generate new system actions, theories from cognitive science/psychology/public health in designing ways to measure what helps users, and algorithms from statistical machine learning & artificial intelligence to conduct experiments and analyse data in real time. 

This is a seminar style course, with a heavy emphasis on students doing research projects that are collaborative, involving the design of randomized experiments by behavioral/social scientists (psychology, public health, education), the deployment and evaluation of software and apps (human-computer interaction), and the application and extension of algorithms/models for dynamic experimentation (statistics, machine learning, operations research).

This course introduces students to computational principles for designing user-facing systems that are intelligent and continually improving, drawing on interdisciplinary work in crowdsourcing, human computation, psychology, experimental design, and statistical machine learning. Students will learn principles for enhancing and personalizing adaptive user-facing systems through randomized A/B experimental comparisons. (Examples of systems include lessons in online courses, activities for mental health, apps for encouraging exercise and other health behavior change, marketing and product design). Students will learn how to design and conduct randomized A/B experiments that are collaborative, dynamic, and personalized. Collaborative experiments can require combining multiple stakeholders in design: drawing on theories from social and behavioral sciences to design alternative versions of a user-facing system (e.g. educational theories about learning, psychology of goal-setting, clinical, and public health insights into cognitive behaviour therapy),  the practical experience of designers, and using crowdsourcing techniques to have users themselves participate in designing improvements to be experimentally evaluated. Dynamic and personalized experiments require using statistics and machine learning or other artificial intelligence techniques to discover in real time which conditions are effective (on average, and for subgroups of users). These models and algorithms may include reinforcement learning, multi-armed (contextual) bandits, Bayesian Optimization and Gaussian Process Regression, and Adaptive Clinical Trials. The course will combine multiple disciplines and involve collaborative work (e.g. teams of students with backgrounds in social and behavioural science, human-computer interaction and crowdsourcing, and statistics or machine learning).

Recommended (not required) preparation is one of the following courses (few students will have multiple): human-computer interaction, psychology/economics/public health/business course on theories of behaviour/learning/decision making, introductory experimental design, machine learning, introductory statistics

Examples of weekly topics are: