About this course: Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.

It's hard to remember the days when artificial intelligence seemed like an intangible, futuristic concept. This has been decades in the making, however, and the past 90 years have seen both renaissances and winters for the field of study. At present, AI is launching a persistent infiltration into our personal lives with the rise of self-driving cars and intelligent personal assistants. In the enterprise, we likewise see AI rearing its head in adaptive marketing and cybersecurity. The rise of AI is exciting, but people often throw the term around in an attempt to win buzzword bingo, rather than to accurately reflect technological capabilities.

About this course: In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we look at how to use these to do fun things with datasets - like how to rotate images of faces and how to extract eigenvectors to look at how the Pagerank algorithm works. Since we're aiming at data-driven applications, we'll be implementing some of these ideas in code, not just on pencil and paper. Towards the end of the course, you'll write code blocks and encounter Jupyter notebooks in Python, but don't worry, these will be quite short, focussed on the concepts, and will guide you through if you've not coded before.

About this course: This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We'll learn about the fundamentals of Linear Algebra and Neural Networks. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras one real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.

About this course: Medical Neuroscience explores the functional organization and neurophysiology of the human central nervous system, while providing a neurobiological framework for understanding human behavior. In this course, you will discover the organization of the neural systems in the brain and spinal cord that mediate sensation, motivate bodily action, and integrate sensorimotor signals with memory, emotion and related faculties of cognition. The overall goal of this course is to provide the foundation for understanding the impairments of sensation, action and cognition that accompany injury, disease or dysfunction in the central nervous system. The course will build upon knowledge acquired through prior studies of cell and molecular biology, general physiology and human anatomy, as we focus primarily on the central nervous system. This online course is designed to include all of the core concepts in neurophysiology and clinical neuroanatomy that would be presented in most first-year neuroscience courses in schools of medicine.

About this course: This course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you're struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track.

About this course: This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the "rise over run" formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be.

This project is cool, but it's an extreme oversimplification of protein folding. And that's fine for what it is. Folding@home attempts to simulate real biomolecular systems in atomic detail, using 3D physics-based molecular models (usually based on approximations to quantum mechanics) and Markov chain Monte Carlo sampling to search geometric/folding configurations with a simulated set of thermodynamic conditions. One could think of Folding@home simulations as a reinforcement learning problem where the reward function is the minimization of Gibbs free energy with respect to the protein's geometry/folding.

As stragglers settle into their seats for general biology class, real-time captions of the professor's banter about general and special senses – "Which receptor picks up pain? An interpreter stands a few feet away and interprets the professor's spoken words into American Sign Language, the primary language used by the deaf in the US. Except for the real-time captions on the screens in front of the room, this is a typical class at the Rochester Institute of Technology in upstate New York. About 1,500 students who are deaf and hard of hearing are an integral part of campus life at the sprawling university, which has 15,000 undergraduates. Nearly 700 of the students who are deaf and hard of hearing take courses with students who are hearing, including several dozen in Sandra Connelly's general biology class of 250 students. The captions on the screens behind Connelly, who wears a headset, are generated by Microsoft Translator, an AI-powered communication technology. The system uses an ...

Technological innovations have impacted almost every aspect of life over the past century. The steam engine and electricity allowed total labor productivity to grow at more than 2 percent per year and the total number of weekly work hours to drop from 60 to 40. The concept of artificial intelligence is not new but has recently come into view as a technology that is capable of revolutionizing the world and bringing a new industrial revolution. A significant amount of research and discussion among scientists and economists has focused on the critical topic of disruption to labor markets and potential productivity gains from AI. However, there is a more nuanced picture of the way in which AI will reshape how we work – in many cases augmenting our abilities and supporting organizations to redefine their operating models for improved performance and agility.