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Online Introduction to Artificial Intelligence is based on Stanford CS221, Introduction to Artificial Intelligence. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. In this course you will learn key concepts in computer science and learn how to write your own computer programs in the context of building a web crawler. At the end of this course you will have a rock solid foundation for programming in Python and built a working web crawler. This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning. Dan Jurafsky is Professor of Linguistics and Professor by Courtesy of Computer Science at Stanford University. How to Build a Blog. Starting from the basics of how the web works, this class will walk you through everything you need to know to build your own blog application and scale it to support large numbers of users. In this project-based course your knowledge will be evaluated as you learn to build your own blog application! Programming Principles Learn new concepts, patterns, and methods that will expand your programming abilities, helping move you from a novice to an expert programmer. Move along the path towards becoming an expert programmer! In this course you will learn how to program in R and how to use R for effective data analysis.
The goal of the course is to help you develop a valuable mental ability – a powerful way of thinking that our ancestors have developed over three thousand years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system. The past decade has witnessed the successful of application of many AI techniques used at `web-scale’, on what are popularly referred to as big data platforms based on the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-SQL databases and stream computing engines. The course covered learning techniques for many different types of neural network including deep feed-forward networks, recurrent networks and Boltzmann Machines.
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications.
This course is an introduction to the key ideas and principles of the collection, display, and analysis of data to guide you in making valid and appropriate conclusions about the world. This course will provide an intuitive introduction to applied statistical reasoning, introducing fundamental statistical skills and acquainting students with the full process of inquiry and evaluation used in investigations in a wide range of fields. Probably the greatest challenge of the “21st century of the brain” is to understand how subcellular and cellular neuronal processes give rise to behavior – movement, perception, emotions, memory and creativity. In this class, you will learn the concepts and methods of linear algebra, and how to use them to think about problems arising in computer science. We introduce the student to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. After being founded just last year, online education startup Coursera turned heads this summer when it raised a $43 million Series B round. The company created by two Stanford professors told AllThingsD it had added $20 million more, with the delay from the previous close attributed to the fact that much of the money was coming from three university partners, who have long due diligence processes. Coursera isn’t naming the three university backers, but said the round also included additional money from existing investors GSV Capital and Learn Capital. Coursera, with 5.5 million students taking classes from 100 universities and institutions, is one of the largest companies in a crop of ed-tech startups. Coursera now has a staff of about 70 people, and it is making some money by selling verified certificates that students can use to show they’ve completed a MOOC.
This class introduces students to the basics of Artificial Intelligence, which includes machine learning, probabilistic reasoning, robotics, and natural language processing. The objective of this class is to teach you modern AI. This course will prepare you to take many of Udacity's more advanced courses. David Evans is a Professor of Computer Science at the University of Virginia where he teaches computer science and leads research in computer security. In this class you will practice going from a problem description to a solution, using a series of assignments. Peter Norvig is Director of Research at Google Inc. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. Online advertising, machine translation, natural language understanding, sentiment mining, personalized medicine, and national security are some examples of such AI-based web-intelligence applications that are already in the public eye.
It will introduce the basic ideas and techniques underlying the design of intelligent computer systems. It covered recent applications to speech, vision, and language, and used hands-on programming assignments. Geoffrey Hinton was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. The course will begin with an overview of how to organize, perform, and write-up data analyses. This course will discuss, step-by-step, how modern molecular, optical, electrical, anatomical and theoretical methods have provided fascinating insights into the operation of the elementary building blocks of brains and, most importantly, how neuronal mechanisms underlie memory and learning processes.


Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You will write small programs in the programming language Python to implement basic matrix and vector functionality and algorithms, and use these to process real-world data to achieve such tasks as: two-dimensional graphics transformations, face morphing, face detection, image transformations such as blurring and edge detection, image perspective removal, audio and image compression, searching within an image or an audio clip, classification of tumors as malignant or benign, integer factorization, error-correcting codes, secret-sharing, network layout, document classification, and computing Pagerank (Google's ranking method). Learn Capital partner Rob Hutter observed that at least 60 technology startups in the education space were venture funded in 2012, coming from a drought just a couple years earlier. You learn about the basic techniques and tricks of the trade, at the same level we teach our Stanford students. He is the author of an introductory computer science textbook and has won Virginia's highest award for university faculty. He is also a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. The course covers practical issues in statistical computing which includes programming in R, reading data into R, creating informative data graphics, accessing R packages, creating R packages with documentation, writing R functions, debugging, and organizing and commenting R code. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. Others, though less apparent, impact the operations of large enterprises from sales and marketing to manufacturing and supply chains. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Then we will cover some of the most popular and widely used statistical methods like linear regression, principal components analysis, cross-validation, and p-values.
You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. And edX, the nonprofit version of Coursera that originated at Harvard and MIT, now seems somewhat more focused on open-source MOOC tools.
It is a fusion of different media styles, different topics, different formats and different sources. He has PhD, SM, and SB degrees from MIT. Udacity courses include lecture videos, quizzes and homework assignments. In 2010, he co-founded Hipmunk, a company to take the agony out of searching for plane and hotel tickets. Topics in statistical data analysis and optimization will provide working examples. Roger D. He received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member in Computer Science at Carnegie-Mellon.
ML has become one of the hottest fields of study today, taken up by graduate and undergraduate students from 15 different majors at Caltech.
Instead of focusing on mathematical details, the lectures will be designed to help you apply these techniques to real data using the R statistical programming language, interpret the results, and diagnose potential problems in your analysis. Howe has received awards from Microsoft Research and honors for papers in scientific data management, and serves on a number of program committees, organizing committees, and advisory boards in the area, including the advisory board of the Data Science certificate program at UW. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures. He was a recipient of the National Science Foundation's Presidential Young Investigator Award, and has received multiple research grants from the National Science Foundation. Whether you are a seasoned professional, a college student, or a curious high school student - everyone can participate. This online class will make this material available to a worldwide audience. Prior to joining Google he was the head of the Computation Sciences Division at NASA Ames Research Center. Peng is an associate professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a Co-Editor of the Simply Statistics blog. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world. Gautam Shroff is Vice President & Chief Scientist, Tata Consultancy Services and heads TCS’ Innovation Lab in Delhi, India, and is teaching this course as in an adjunct capacity at IIT Delhi and IIIT Delhi.
He then moved to the Department of Computer Science at the University of Toronto where he directs the program on "Neural Computation and Adaptive Perception” for the Canadian Institute for Advanced Research.
This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.


You will also have the opportunity to critique and assist your fellow classmates with their data analyses. Jeff Leek is an Assistant Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and co-editor of the Simply Statistics Blog.
We will end by discussing emerging frontiers in brain research, including the interaction between brain research and the arts. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? He has been made an ACM Fellow in recognition of his contributions to research on graph algorithms.
At Stanford, he teaches Machine Learning, which with a typical enrollment of 350 Stanford students, is among the most popular classes on campus.
Each nugget is roughly five minutes or less, giving you the chance to learn piece by piece and re-watch short lesson portions with ease.
He created the course Statistical Programming at Johns Hopkins where it has been taught for the past 8 years. The lectures follow each other in a story-like fashion, with the main topics listed below. Yaser S. He created Data Analysis as a component of the year-long statistical methods core sequence for Biostatistics students at Johns Hopkins.
As an added bonus, a lecture on perception, action, cognition and emotions will be taught by an acclaimed neuroscientists, Prof. You'll then put this skill to use in a programming assignment aimed at producing an intelligent hex player. Ira Pohl is a Professor of Computer Science at the University of California, Santa Cruz, and a Fellow of the ACM. You will do homework assignments, take exams, participate in discussions with other students, ask questions of the instructors, and also get a final score. His research is primarily on machine learning, artificial intelligence, and robotics, and most universities doing robotics research now do so using a software platform (ROS) from his group.
Quizzes are embedded within the lecture videos and are meant to let you check-in with how completely you are digesting the course information.
Keith Devlin is a co-founder and Executive Director of Stanford University's H-STAR institute and a co-founder of the Stanford Media X research network.
Shroff had been on the faculty of the California Institute of Technology, Pasadena, USA and thereafter of the Department of Computer Science and Engineering at Indian Institute of Technology, Delhi, India.
Abu-Mostafa is a Professor of Electrical Engineering and Computer Science at the California Institute of Technology. The course has won a teaching excellence award, voted on by the students at Johns Hopkins, every year Dr. Peng is also a national leader in the area of methods and standards for reproducible research and is the Reproducible Research editor for the journal Biostatistics. He is a World Economic Forum Fellow and a Fellow of the American Association for the Advancement of Science. He has also held visiting positions at NASA Ames Research Center in Mountain View, CA, and at Argonne National Labs in Chicago. His main fields of expertise are machine learning and computational finance. A real Caltech course, not a watered-down version. Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers? Tim Roughgarden is an Associate Professor of Computer Science and (by courtesy) Management Science and Engineering at Stanford University, where he holds the Chambers Faculty Scholar development chair. At Stanford, he has taught the Design and Analysis of Algorithms course for the past eight years. He is a recipient of the Pythagoras Prize, the Peano Prize, the Carl Sagan Award, and the Joint Policy Board for Mathematics Communications Award.
His research concerns the theory and applications of algorithms, especially for networks, auctions and other game-theoretic applications, and data privacy. In 2003, he was recognized by the California State Assembly for his "innovative work and longtime service in the field of mathematics and its relation to logic and linguistics." He is "the Math Guy" on National Public Radio. For his research, he has been awarded the ACM Grace Murray Hopper Award, the Presidential Early Career Award for Scientists and Engineers (PECASE), the Shapley Lecturership of the Game Theory Society, a Sloan Fellowship, INFORM's Optimization Prize for Young Researchers, and the Mathematical Programming Society's Tucker Prize.



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