Now we'll put physical links and joints together and consider the geometry and the physics required to understand their coordinated motion. We'll learn about the geometry of degrees of freedom. We'll then go back to Newton and learn a compact way to write down the physical dynamics that describes the positions, velocities and accelerations of those degrees of freedom when forced by our actuators.Of course there are many different ways to put limbs and bodies together: again, the animals can teach us a lot as we consider the best morphology for our limbed robots. Sprawled posture runners like cockroaches have six legs which typically move in a stereotyped pattern which we will consider as a model for a hexapedal machine. Nature's quadrupeds have their own varied gait patterns which we will match up to various four-legged robot designs as well.

Column n The Educational Advances in Artificial Intelligence column discusses and shares innovative educational approaches that teach or leverage AI and its many subfields at all levels of education (K-12, undergraduate, and graduate levels). In this column I describe my experience adapting the content and infrastructure from massive, open, online courses (MOOCs) to enhance my courses in the Department of Electrical Engineering and Computer Science at Vanderbilt University. I begin with my informal, early use of MOOC content and then move to two deliberatively designed strategies for adapting MOOCs to campus (that is, wrappers and small private online classes [SPOCs]). I describe student reactions and touch on selected policy and institutional considerations. In the never-ending search for increasing student bang-for-the-buck, I was motivated to increase the bang, rather than reduce the buck, the latter being well above my pay grade.

The R language is best suited for statistical computations and visualization. Even if you do not have any prior experience in programming or statistical software, this Learning Path will help you get you up and running not only with the basics of R but also statistically modeling. This learning journey begin by introducing R and setting things up so that you are ready to go using RStudio, the associated IDE. Then, you will look at R as a programming language and see how the standard things are done in it. You will obtain a dataset and then learn how to clean the dataset.

For those who haven't heard of it, EAAI is a symposium that is held in conjunction with AAAI. The symposium provides a venue for researchers and educators to discuss pedagogical issues and share resources related to AI and education. This year, the symposium featured a range of activities, including two invited talks, paper presentations, poster presentations, panels, and workshops. Several main themes of discussion at the symposium included the introduction of AI concepts in early courses, active learning, and massive open online courses (MOOCs) and flipped classrooms. With the emergence of "big data" as a buzzword in the mainstream media, new students are often interested in learning about this area but may not have the math or computing skills to support their interests.

In this video course, you will learn to tap some of the powerful abilities of R. R is one of the leading packages in the world with a vast number of active users and, as a result, has a massive number of state-of-the-art libraries. You will master the basics and get comfortable with R, so you can then use its libraries to do the heavy-lifting. You'll begin by looking at high-performance computing in the classic, computationally intensive scenario: finding prime numbers.Then you'll learn how to use R, before moving on to using C, which is far faster. Next you will use the power of parallel, though that varies from problem to problem since some are more suitable for parallelization. Then you will look at some powerful options available on R where you don't just produce a static result but instead respond to user selections.

Data Scientists are most in demand & enjoy one of the top-paying jobs in the industry, with an average salary of $120,000 as per the data from Glassdoor and Indeed. So whom is the course for? If you are a student, an IT professional, an analyst, a scientist or an academic and you're looking to make the transition to data science, or you're a student, and you want to learn what data science is all about. If you've got some programming or scripting knowledge, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This course is a short course on Data science to enable you to start learning & using the fundamentals immediately.

About this course: Machine learning is transforming the world around us. To become successful, you'd better know what kinds of problems can be solved with machine learning, and how they can be solved. Don't know where to start? The answer is one button away. During this course you will: - Identify practical problems which can be solved with machine learning - Build, tune and apply linear models with Spark MLLib - Understand methods of text processing - Fit decision trees and boost them with ensemble learning - Construct your own recommender system.

About this course: For many years now, people have been improving their tools, studying the forces of nature and bringing them under control, using the energy of the nature to operate their machines. Last century is noted for the creation of machines which can operate other machines. Nowadays the creation of devices that interact with the physical world is available to anyone. Our course consists of a series of practical problems on making things that work independently: they make their own decisions, act, move, communicate with each other and people around, and control other devices. We will demonstrate how to assemble such devices and programme them using the Arduino platform as a basis.

We introduce an efficient algorithmic framework for model selection in online learning, also known as parameter-free online learning. Departing from previous work, which has focused on highly structured function classes such as nested balls in Hilbert space, we propose a generic meta-algorithm framework that achieves online model selection oracle inequalities under minimal structural assumptions. We give the first computationally efficient parameter-free algorithms that work in arbitrary Banach spaces under mild smoothness assumptions; previous results applied only to Hilbert spaces. We further derive new oracle inequalities for matrix classes, non-nested convex sets, and $\mathbb{R}^{d}$ with generic regularizers. Finally, we generalize these results by providing oracle inequalities for arbitrary non-linear classes in the online supervised learning model. These results are all derived through a unified meta-algorithm scheme using a novel "multi-scale" algorithm for prediction with expert advice based on random playout, which may be of independent interest.

Machine learning (ML) is touted as the most critical skill of current times. Artificial intelligence (AI), an application of ML, is becoming pervasive. From autonomous vehicles to self-tuned databases, AI and ML are found everywhere. Industry analysts often refer to AI-driven automation as the job killer. Almost every domain and industry vertical are getting impacted by AI and ML.