We present a novel approach to control design for nonlinear systems, which leverages reinforcement learning techniques to learn a linearizing controller for a physical plant with unknown dynamics. Feedback linearization is a technique from nonlinear control which renders the input-output dynamics of a nonlinear plant \emph{linear} under application of an appropriate feedback controller. Once a linearizing controller has been constructed, desired output trajectories for the nonlinear plant can be tracked using a variety of linear control techniques. A single learned policy then serves to track arbitrary desired reference signals provided by a higher-level planner. We present theoretical results which provide conditions under which the learning problem has a unique solution which exactly linearizes the plant. We demonstrate the performance of our approach on two simulated problems and a physical robotic platform. For the simulated environments, we observe that the learned feedback linearizing policies can achieve arbitrary tracking of reference trajectories for a fully actuated double pendulum and a 14 dimensional quadrotor. In hardware, we demonstrate that our approach significantly improves tracking performance on a 7-DOF Baxter robot after less than two hours of training.

Convolutional neural networks (CNNs) have been increasingly deployed to edge devices. Hence, many efforts have been made towards efficient CNN inference on resource-constrained platforms. This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-device training? We strive to reduce the energy cost during training, by dropping unnecessary computations, from three complementary levels: stochastic mini-batch dropping on the data level; selective layer update on the model level; and sign prediction for low-cost, low-precision back-propagation, on the algorithm level. Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training. For example, when training ResNet-74 on CIFAR-10, we achieve aggressive energy savings of >90% and >60%, while incurring a top-1 accuracy loss of only about 2% and 1.2%, respectively. When training ResNet-110 on CIFAR-100, an over 84% training energy saving is achieved without degrading inference accuracy.

I consider two problems in machine learning and statistics: the problem of estimating the joint probability density of a collection of random variables, known as density estimation, and the problem of inferring model parameters when their likelihood is intractable, known as likelihood-free inference. The contribution of the thesis is a set of new methods for addressing these problems that are based on recent advances in neural networks and deep learning.

Can machines trace human knowledge like humans? Knowledge tracing (KT) is a fundamental task in a wide range of applications in education, such as massive open online courses (MOOCs), intelligent tutoring systems, educational games, and learning management systems. It models dynamics in a student's knowledge states in relation to different learning concepts through their interactions with learning activities. Recently, several attempts have been made to use deep learning models for tackling the KT problem. Although these deep learning models have shown promising results, they have limitations: either lack the ability to go deeper to trace how specific concepts in a knowledge state are mastered by a student, or fail to capture long-term dependencies in an exercise sequence. In this paper, we address these limitations by proposing a novel deep learning model for knowledge tracing, namely Sequential Key-Value Memory Networks (SKVMN). This model unifies the strengths of recurrent modelling capacity and memory capacity of the existing deep learning KT models for modelling student learning. We have extensively evaluated our proposed model on five benchmark datasets. The experimental results show that (1) SKVMN outperforms the state-of-the-art KT models on all datasets, (2) SKVMN can better discover the correlation between latent concepts and questions, and (3) SKVMN can trace the knowledge state of students dynamics, and a leverage sequential dependencies in an exercise sequence for improved predication accuracy.

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The best job in the United States is machine-learning engineer, according to job posting site Indeed.com. With an average salary close to $150,000 and a whopping 344% growth in job postings, these engineers are in high demand throughout the country. Including stock compensation, "A.I. specialists with little or no industry experience can make between $300,000 and $500,000 a year," according to The New York Times. Beyond that, the federal government needs trained data scientists to help sort through one of the largest repositories of information in the world. Every agency relies on these specialists to compile and analyze data that affects everything from major national infrastructure plans to disaster relief in the face of hurricanes and wildfires.

Major League Baseball has teamed with creative software developer Adobe to offer dozens of business school students access to data on fan behavior as part of the software giant's yearly analytics competition. For a chance at $60,000 in cash and prizes, the students will analyze the information, which includes stats like in-game purchases, web traffic and customer drop-off tallies, and distill it into recommendations for how the league can better expand its in-person stadium and retail experience to its digital properties. This year's contest will be the first in the decade-old Adobe Analytics Challenge to include machine learning software among the tools to which students have access, namely Adobe Sensei, the artificial intelligence engine that powers much of the creative software giant's customer targeting and predictive analytics suite. Specifically, students will look for anomalies and behavioral patterns in the data that might point to elements of the MLB's digital user experience that are driving people away, or particularly successful features upon which the league's developers should expand. The data is segmented by customer demographics and spans the MLB's flagship website, mobile apps and other digital properties.

A team of scientists from research centers stretching from Maine to South Carolina will develop and deploy high-tech tools to explore cyanobacteria in lakes across the East Coast. The multi-year project will combine big data, artificial intelligence and robotics with new and time-tested techniques for lake sampling to understand where, when, and how cyanobacterial blooms develop. The research team brings together experts in freshwater ecology, computer science, engineering and geospatial science from Bates College, Colby College, Dartmouth, the University of New Hampshire, the University of Rhode Island and the University of South Carolina. "It is rare to have teams from so many different specialties converge to study a problem like this," said Alberto Quattrini Li, an assistant professor of computer science at Dartmouth and the overall project lead. "By working together, we can increase the amount of data that can be collected and increase prediction capabilities."

I am actually not even aware of any machine learning (ML) problem that is considered to have been solved recently or in the past. This tells you a lot about how hard things really are in ML. Of course, if you read media outlets, it may seem like researchers are sweeping the floor clean with deep learning (DL), solving ML problems one after the other leaving no stones unturned. In reality, they are not, researchers actually attack relatively simpler problems in the hope of collectively solving the bigger problems, that is just how research works. You can see DeepMind aim is to "solve intelligence and make the world a better place" but they are busy building game playing algorithms.

The demand for Data Scientists is immense. In this course, you'll learn how you can play a part in fulfilling this demand and build a long, successful career for yourself. The #1 goal of this course is clear: give you all the skills you need to be a Data Scientist who could start the job tomorrow... within 6 weeks. With so much ground to cover, we've stripped out the fluff and geared the lessons to focus 100% on preparing you as a Data Scientist. You'll discover: * The structured path for rapidly acquiring Data Science expertise * How to build your ability in statistics to help interpret and analyse data more effectively * How to perform visualizations using one of the industry's most popular tools * How to apply machine learning algorithms with Python to solve real world problems * Why the cloud is important for Data Scientists and how to use it Along with much more.