In Fall 2014 we offered an online course on Knowledge-Based Artificial Intelligence (KBAI) to about 200 students as part of the Georgia Tech Online MS in CS program. By now we have offered the course to more than 1000 students. We describe the design, development and delivery of the online KBAI class in Fall 2014.

We have designed an open and modular course for data science and big data analytics using a workflow paradigm that allows students to easily experience big data through a sophisticated yet easy to use instrument that is an intelligent workflow system. A key aspect of this work is the use of semantic workflows to capture and reuse end-to-end analytic methods that experts would use to analyze big data, and the use of an intelligent workflow system to elaborate the workflow and manage its execution and resulting datasets. Through the exposure of big data analytics in a workflow framework, students will be able to get first-hand experiences with a breadth of big data topics, including multi-step data analytic and statistical methods, software reuse and composition, parallel distributed programming, high-end computing. In addition, students learn about a range of topics in AI, including semantic representations and ontologies, machine learning, natural language processing, and image analysis.

Effective training of deep neural networks suffers from two main issues. The first is that the parameter space of these models exhibit pathological curvature. Recent methods address this problem by using adaptive preconditioning for Stochastic Gradient Descent (SGD). These methods improve convergence by adapting to the local geometry of parameter space. A second issue is overfitting, which is typically addressed by early stopping. However, recent work has demonstrated that Bayesian model averaging mitigates this problem. The posterior can be sampled by using Stochastic Gradient Langevin Dynamics (SGLD). However, the rapidly changing curvature renders default SGLD methods inefficient. Here, we propose combining adaptive preconditioners with SGLD. In support of this idea, we give theoretical properties on asymptotic convergence and predictive risk. We also provide empirical results for Logistic Regression, Feedforward Neural Nets, and Convolutional Neural Nets, demonstrating that our preconditioned SGLD method gives state-of-the-art performance on these models.

This paper adapts topic models to the psychometric testing of MOOC students based on their online forum postings. Measurement theory from education and psychology provides statistical models for quantifying a person's attainment of intangible attributes such as attitudes, abilities or intelligence. Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions. The set of items can be used to measure a latent skill if individuals' responses on them conform to a Guttman scale. Such well-scaled items differentiate between individuals and inferred levels span the entire range from most basic to the advanced. In practice, education researchers manually devise items (quiz questions) while optimising well-scaled conformance. Due to the costly nature and expert requirements of this process, psychometric testing has found limited use in everyday teaching. We aim to develop usable measurement models for highly-instrumented MOOC delivery platforms, by using participation in automatically-extracted online forum topics as items. The challenge is to formalise the Guttman scale educational constraint and incorporate it into topic models. To favour topics that automatically conform to a Guttman scale, we introduce a novel regularisation into non-negative matrix factorisation-based topic modelling. We demonstrate the suitability of our approach with both quantitative experiments on three Coursera MOOCs, and with a qualitative survey of topic interpretability on two MOOCs by domain expert interviews.

We study an important crowdsourcing setting where agents evaluate one another and, based on these evaluations, a subset of agents are selected. This setting is ubiquitous when peer review is used for distributing awards in a team, allocating funding to scientists, and selecting publications for conferences. The fundamental challenge when applying crowdsourcing in these settings is that agents may misreport their reviews of others to increase their chances of being selected. We propose a new strategyproof (impartial) mechanism called Dollar Partition that satisfies desirable axiomatic properties. We then show, using a detailed experiment with parameter values derived from target real world domains, that our mechanism performs better on average, and in the worst case, than other strategyproof mechanisms in the literature.

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively in many fields such as search engines, robotics, self-driving cars, and more. Through this book, you will learn how to perform various machine learning tasks in a range of environments. If you want to develop machine learning applications or implement machine learning in existing systems, Python is an excellent language to do so. Machine learning with Python is currently the most used standard to perform machine learning and is a great alternative for developers because of its wide selection of libraries and developer-friendly ecosystem.

It's day one at the Singularity University: the opening address has just been delivered by a hologram. Craig Venter, who was one of the first scientists to sequence the human genome and created the first synthetic life form, is up next. And later, we will see two people, paralysed from the waist down, use robotic exoskeletons to rise up and walk. But first, the co-founder of the Singularity University, Peter Diamandis, gives us our instructions for the day. Your task, he says, is to pick one of the "grand challenges of humanity" – the lack of clean drinking water, say. And then come up with an idea that "can positively impact the lives of a billion people". Some of us haven't even had coffee yet. There's about 50 of us present and the room has been divided up into tables, one for education, another for poverty, another for water, and I'm not sure where I should sit. Diane Murphy, the university's PR executive, hesitates for a moment and then directs me over to the table marked "food". "Tell you what," she says.

This semester, as a reflective practice and an opportunity to continue my hobby (filmmaking), I decided to create a weekly video log (or vlog) of what has been happening in my middle school robotics class. The following was episode 4 of the series aptly entitled'Middle School Robotics.' This video was a bit different than the others as I sort of gave an overview of the course, my philosophy behind it, and the practices that I've seen bear the most fruit in the class. Most of my project resources were taken from this website: http://ev3lessons.com/lessons.html

Welcome to the 8th part of our machine learning regression tutorial within our Machine Learning with Python tutorial series. Where we left off, we had just realized that we needed to replicate some non-trivial algorithms into Python code in an attempt to calculate a best-fit line for a given dataset. Before we embark on that, why are we going to bother with all of this? Linear Regression is basically the brick to the machine learning building. It is used in almost every single major machine learning algorithm, so an understanding of it will help you to get the foundation for most major machine learning algorithms. For the enthusiastic among us, understanding linear regression and general linear algebra is the first step towards writing your own custom machine learning algorithms and branching out into the bleeding edge of machine learning, using what ever the best processing is at the time.

In keeping with its vision of an era of cognitive computing enabled by acceleration technology, IBM Research (NYSE: IBM) today announced plans for a multi-year collaboration with the University of Illinois Urbana-Champaign to create the Center for Cognitive Computing Systems Research (C3SR) which will be housed within the College of Engineering on the Urbana campus. IBM has big ambitions for the center: "C3SR will build and optimize integrated systems such as state-of-the-art cognitive computing systems modeled on IBM's Watson technology that can master a subject area by learning from multimedia and multi-modal educational content. Such systems will efficiently ingest vast amounts of data including videos, lecture notes, homework, and textbooks, and reason through this knowledge effectively enough to be able to eventually pass a college level exam." Many details are yet to be worked out. The level of funding and size of installation will be announced this summer when the new center formally opens, said Hillery Hunter, a project driver and the director for systems acceleration and memory at IBM Research.