Readme file for Web Design and Objects
This readme file contains details of links to all the Web Design and Objects module's material held on Jorum and information about the module as well.
Readme file for Web Design and Objects
This readme file contains details of links to all the Web Design and Objects module's material held on Jorum and information about the module as well.
Introduction to Artificial Intelligence - Neural Networks
This reading material forms part of the "Neural Networks" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Neural Networks
This reading material forms part of the "Neural Networks" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Knowledge Representation
This reading material forms part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Knowledge Representation
This reading material forms part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
Readme file for Distributed Web Systems
This readme file contains details of links to all the Distributed Web Systems module's material held on Jorum and information about the module as well.
Readme file for Distributed Web Systems
This readme file contains details of links to all the Distributed Web Systems module's material held on Jorum and information about the module as well.
Introduction to Artificial Intelligence - Knowledge Representation
This practical forms part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Knowledge Representation
This practical forms part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Knowledge Representation
This practical forms part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Knowledge Representation
This practical forms part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Knowledge Representation
This practical forms part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Knowledge Representation
This practical forms part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Knowledge Representation
These notes form part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
Introduction to Artificial Intelligence - Knowledge Representation
These notes form part of the "Knowledge Representation" topic in the Introduction to Artificial Intelligence module.
2.017J Design of Electromechanical Robotic Systems (MIT)
This course covers the design, construction, and testing of field robotic systems, through team projects with each student responsible for a specific subsystem. Projects focus on electronics, instrumentation, and machine elements. Design for operation in uncertain conditions is a focus point, with ocean waves and marine structures as a central theme. Topics include basic statistics, linear systems, Fourier transforms, random processes, spectra, ethics in engineering practice, and extreme events
14.30 Introduction to Statistical Method in Economics (MIT)
This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.
14.30 Introduction to Statistical Method in Economics (MIT)
This course will provide a solid foundation in probability and statistics for economists and other social scientists. We will emphasize topics needed in the further study of econometrics and provide basic preparation for 14.32. No prior preparation in probability and statistics is required, but familiarity with basic algebra and calculus is assumed.
18.440 Probability and Random Variables (MIT)
This course introduces students to probability and random variable. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.