We will learn about the Gaussian distribution for parametric modeling in robotics. The Gaussian distribution is the most widely used continuous distribution and provides a useful way to estimate uncertainty and predict in the world. We will start by discussing the one-dimensional Gaussian distribution, and then move on to the multivariate Gaussian distribution. Finally, we will extend the concept to models that use Mixtures of Gaussians.

About this course: Welcome to Course 2 - Sales Strategy - This course is designed to discuss the application of intelligence analysis in the sales planning process. And this approach contributes to integrating the sales planning process into the corporate strategy of the company because, in the strategy analysis and formulation process, we apply models, frameworks, tools, and techniques that also apply to the sales planning and management process. Therefore, the expected outcomes of this course focus on the transition from traditional to strategic sales planning, by discussing and applying the concepts recommended to support the development of the strategic guidelines. The concepts, models, tools, and techniques discussed and practiced during the course focus on the improvement of value creation from the sales function empowered by intelligence analysis, a process which typically applies in the strategy analysis front. The discussions go through how intelligence analysis can support the sales function, by providing methods to connect strategy to marketing and sales planning processes.

R is on the rise and showing itself as a powerful option in many software development domains. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis, creating high-level graphics, and machine learning. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way. The course is structured in simple lessons so that the learning process feels like a step-by-step guide to plotting. We start by importing data in R from popular formats such as CSV and Excel tables.

Editor's Note: This Commentary is part of a special report exploring game-changing trends and innovations that have the potential to shake up the schoolhouse. Artificial intelligence is a rapidly emerging technology that has the potential to change our everyday lives with a scope and speed that humankind has never experienced before. Some well-known technology leaders such as Tesla architect Elon Musk consider AI a potential threat to humanity and have pushed for its regulation "before it's too late"--an alarmist statement that confuses AI science with science fiction. What is the reality behind these concerns, and how can educators best prepare for a future with artificial intelligence as an inevitable part of our lives? General, widespread legislative regulation of AI is not going to be the right way to prepare our society for these changes.

Machine learning vs. deep learning isn't exactly a boxing knockout – deep learning is a subset of machine learning, and both are subsets of artificial intelligence (AI). However, there is a lot of confusion in the marketplace around the definitions and use cases of machine learning and deep learning, so let's clear up the confusion. Computers identify and act upon data patterns, and over time learn to improve their accuracy without explicit programming. Machine learning is behind analytics like predictive coding, clustering, and visual heat maps. Deep learning computer networks simulate the way a human brain perceives, organizes, and makes decisions from data input.

It is no doubt that the sub-field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix's algorithms to make movie suggestions based on movies you have watched in the past or Amazon's algorithms that recommend books based on books you have bought before. So if you want to learn more about machine learning, how do you start? For me, my first introduction is when I took an Artificial Intelligence class when I was studying abroad in Copenhagen. My lecturer is a full-time Applied Math and CS professor at the Technical University of Denmark, in which his research areas are logic and artificial, focusing primarily on the use of logic to model human-like planning, reasoning and problem solving.

Smartphone and tablet computer apps are seeing increasing use in Japanese schools as teachers look to capitalize on what has become many young people's preferred window to the world. Artificial intelligence-assisted apps have become prevalent in education, particularly in subjects many Japanese teachers struggle to teach well. One subject educators need help with is teaching English, a task that will become all the more important when speaking ability enters the joint achievement test in 2020, part of Japan's high-pressure university entrance exams. Nippon Sports Science University Kashiwa High School in Chiba Prefecture uses an app called TerraTalk to help students improve their English conversation skills. The school introduced the app last summer for use by students planning to study abroad.

Regardless of where you stand on the matter of Data Science sexiness, it's simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. Drawing on their vast stores of employment data and employee feedback, Glassdoor ranked Data Scientist #1 in their 25 Best Jobs in America list. So the role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers -- and the companies that hire them -- Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress. While having a strong coding ability is important, data science isn't all about software engineering (in fact, have a good familiarity with Python and you're good to go). Data scientists live at the intersection of coding, statistics, and critical thinking.

Machine learning is needed for tasks that are too complex for humans to code directly. Some tasks are so complex that it is impractical, if not impossible, for humans to work out all of the nuances and code for them explicitly. So instead, we provide a large amount of data to a machine learning algorithm and let the algorithm work it out by exploring that data and searching for a model that will achieve what the programmers have set it out to achieve. Let's look at these 2 examples: Then comes the Machine Learning Approach: Instead of writing a program by hand for each specific task, we collect lots of examples that specify the correct output for a given input. A machine learning algorithm then takes these examples and produces a program that does the job. The program produced by the learning algorithm may look very different from a typical hand-written program.

Student radicalization on college campuses is a growing concern and the same process is ongoing even in high schools now. Educational institutions often are wonderful places for lively discussion about the world, however at times students can become ill informed via online sources. While computers are supposed to be used in such institutions for educational purposes, students often surf the web there. There are also cases where personal devices (laptops and mobile devices) are used at school for browsing activity. What would seem like a harmless activity may be isolating, polarizing, and radicalizing students.