One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.

Artificial Intelligence is reshaping your relationship with the world and it's just getting started. Tesla's autopilot, job automation, the products you'stumble upon' online - it's entering our daily lives, careers, businesses, even our homes with such blistering pace you probably haven't even realized it. There's a reason Andrew Ng, the founder of $100m company Coursera said "Artificial Intelligence is the new electricity" - soon it'll be as much a part of your daily life as your smartphone, except without the off button. But here's where things get really crazy. This time round, the revolution will see machines taking on tasks no human intellect could ever perform.

Digital technologies like the internet and smartphones are transforming our lives and society. They are proving to be powerful tools for liberating individuals' creative and entrepreneurial potential, as well as providing new educational opportunities and higher wages for marginalized people, both in the U.S. and around the globe. Unfortunately, in the U.S., outdated government regulations and weak consumer protections are undermining these opportunities. What's more, the Trump administration has not yet made significant moves to address this growing crisis: As of this writing, five key White House positions are vacant, without even acting directors or interim leaders to help the executive branch formulate U.S. science and technology policy. As the founder of both the Open Technology Institute and the X-Lab policy and innovation organization, I have spent years at the heart of many Washington, D.C. battles over technology policy, fighting for ideas that would best serve American workers and the general public.

Conventionally, the questions on a test are assumed to be kept secret from test takers until the test. However, for tests that are taken on a large scale, particularly asynchronously, this is very hard to achieve. For example, TOEFL iBT and driver's license test questions are easily found online. This also appears likely to become an issue for Massive Open Online Courses (MOOCs, as offered for example by Coursera, Udacity, and edX). Specifically, the test result may not reflect the true ability of a test taker if questions are leaked beforehand. In this paper, we take the loss of confidentiality as a fact. Even so, not all hope is lost as the test taker can memorize only a limited set of questions' answers, and the tester can randomize which questions to let appear on the test. We model this as a Stackelberg game, where the tester commits to a mixed strategy and the follower responds. Informally, the goal of the tester is to best reveal the true ability of a test taker, while the test taker tries to maximize the test result (pass probability or score). We provide an exponential-size linear program formulation that computes the optimal test strategy, prove several NP-hardness results on computing optimal test strategies in general, and give efficient algorithms for special cases (scored tests and single-question tests). Experiments are also provided for those proposed algorithms to show their scalability and the increase of the tester's utility relative to that of the uniform-at-random strategy. The increase is quite significant when questions have some correlation---for example, when a test taker who can solve a harder question can always solve easier questions.

Rich Sutton is old school king of RL.

By 2020, 25% of the American workforce will be over the age of 55 and approaching retirement, a phenomenon becoming known as the Silver Tsunami. While this could create a shortage of skilled workers in a number of fields including electric utilities, telecommunications, and manufacturing, augmented reality (AR) is poised not only to address issues faced by our aging workforce, but to fundamentality increase productivity by changing how all employees are trained in the future. In 2016, U.S. companies across industries spent nearly $1,000 in training per employee, largely delivered in traditional formats like classroom-based seminars and classes, and even online training modules that mimic that experience. This kind of learning has suited people's needs for centuries, particularly when learning was thought of as memorization with many cultures celebrating those who could recite long texts with exceptional rote skills. But as the breadth of human knowledge expanded, learning paradigms have changed with the works of John Dewey and others who recognized that understanding why information is important and how it relates to our world is true learning--and should be the goal.

The phrase "Machine Learning" refers to the automatic detection of meaningful data by computing systems. In the last few decades, it has become a common tool in almost any task that needs to understand data from large data sets. One of the biggest application of machine learning technology is the search engine. Search engines learn how to provide the best results based on historic, trending, and relative data sets. When you look at anti-spam software, it learns how to filter email messages.

Which machine learning algorithm should I use?

Clustering is a data segmentation technique that divides huge datasets into different groups on the basis of similarity in the data. It is a statistical operation of grouping objects. The resulting groups are clusters.

Please check Piazza regularly for announcements and discussion. I will attempt to post slides before lecture. Readings in CIML are required. Other readings are optional unless otherwise specified. Domingos, Pedro Domingos' video lectures on Coursera There are many excellent machine learning textbooks, but none of them is quite perfect for this class.