This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab/Octave/Python demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information.
Learn Machine Learning Stanford University Professor and earn certification to full proof your career. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.
Automated essay scoring (AES) is a broadly used application of machine learning, with a long history of real-world use that impacts high-stakes decision-making for students. However, defensibility arguments in this space have typically been rooted in hand-crafted features and psychometrics research, which are a poor fit for recent advances in AI research and more formative classroom use of the technology. This paper proposes a framework for evaluating automated essay scoring models trained with more modern algorithms, used in a classroom setting; that framework is then applied to evaluate an existing product, Turnitin Revision Assistant.
When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Coursera co-founder ANDREW NG, adjunct professor of computer science, says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is." Ng, who is chief scientist at Baidu Research, spoke to the Graduate School of Business community as part of a series presented by the Stanford MSx Program, which offers experienced leaders a one-year, full-time learning experience.
Education has mostly followed the same structure for centuries -- e.g., the "sage on a stage" and "assembly line" models. As AI continues to disrupt industries like consumer electronics, ecommerce, media, transportation, and healthcare, is education the next big opportunity? Given that education is the foundation that prepares people to pursue advancements in all the other fields, it has the potential to be the most impactful application of AI. The three segments of the education market -- K-12, higher education, and corporate training -- are going through transitions. In the K-12 market, we are seeing the effect of the newer, more rigorous academic standards (Common Core, Next Generation Science Standards) shifting the focus toward measuring students' critical thinking and problem-solving skills and preparing them for college and career success in the 21st century.