AI will be a driver for our future

Dr. Fei Fei Li, the Director of Stanford’s Artificial Intelligence Lab, took the stage at Innovate! and Celebrate to welcome entrepreneurs, founders, and attendees to the conference with an in-depth discussion of artificial intelligence and explored how AI will be a driver for our future. She discusses what AI technology is already being developed, the challenges scientists are still facing, and the potential consequences for every industry and almost every facet of

What is Machine Learning?

The goals of AI is to create a machine which can mimic a human mind and to do that it needs learning capabilities. But how does machine learning work? Technically speaking, machine learning is all about testing, testing, testing. It starts with training data and then it applies predictions to what you might like. When you make a predicted selection, your answer is so noted. Machine learning is a subfield

Unsupervised Learning :The Next Frontier in AI

Yann LeCun, Director of AI Research at Facebook and Professor of Computer Science at New York University is speaking on the next frontier in Artificial Intelligence. The rapid progress of AI in the last few years are largely the result of advances in deep learning and neural nets, combined with the availability of large datasets and fast GPUs. We now have systems that can recognize images with an accuracy that rivals

Artificial Intelligence by Prof. Deepak Khemani

Mod-01 Lec-01 Artificial Intelligence: Introduction Mod-01 Lec-02 Introduction to AI Mod-01 Lec-03 AI Introduction Philosophy Mod-01 Lec-04 AI Introduction Mod-01 Lec-05 Introduction Philosophy Mod-01 Lec-06 State Space Search Intro Mod-01 Lec-07 Search-DFS and BFS Mod-01 Lec-8 Search DFID Mod-01 Lec-9 Heuristic Search Mod-01 Lec-10 Hill Climbing Mod-01 Lec-11 Solution Space Search,Beam Search Mod-01 Lec-12 TSP Greedy Methods Mod-01 Lec-13 Tabu Search Mod-01 Lec-14 Optimization I (Simulated Annealing) Mod-01 Lec-15 Optimization

Artificial Intelligence Complete Lectures (01-23)

Prof. Patrick Henry Winston introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of this course, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Artificial Intelligence Lectures (01-13) - Barbara Hecker, PhD

Artificial Intelligence course introduces the foundation of simulating or crating intelligence from a computational point of view. It covers the techniques of reduction, reasoning, problem solving, knowledge representation, and machine learning. In addition, it covers applications of decision trees, neural Networks, support vector machines and other learning paradigms. Barbara Hecker, J.D., Ph.D. Barbara is an entrepreneur with a passion for teaching. She’s been a Department Chair, Chief Academic Officer and

The Future of Robotics and Artificial Intelligence

Andrew Ng (Stanford University) is building robots to improve the lives of millions. From autonomous helicopters to robotic perception, Ng’s research in machine learning and artificial intelligence could result one day in a robot that can clean your house.