MTH 453/553 – Homework 2 1. Consider the following two point boundary value problem: u′′(x) = − sin(x) u(0)=0, u(π)=π (a) Using the second order, centered finite difference scheme discussed in class, discretze this two point boundary value problem. What is the resulting system of linear equations? (b) Compute the error constants in the truncation […]
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CMPSC 450 Concurrent Scientific Programming Introduction CMPSC 450 Welcome to the class! • Class meets MWF 8-8:50AM on Zoom • Office hours • Tuesdays and Thursdays 8PM – 9PM. On Zoom. • By appointment • Email: use Canvas • About me: • Master of Engineering, CSE, Penn State 2001 • 20 years industry experience •
EEEE3120: Control System and System Dynamics 2020- 2021 Module Convenor: Ts. Dr. Vimal Rau Aparow Coursework: Exploration of Forward and Inverse Kinematic Using the Articulated Robotic Manipulator Overview This course work counts towards 20% of the module total. Upon completion of the tasks below you will be required to submit a short report summarising the
Numerical Computing, Spring 2022 Homework Assignment 5 QR Factorization via Householder Reduction Be sure to read A&G, section 6.2 and 6.3 (you can skip the subsections on Gram-Schmidt and the SVD) as well as my notes on QR factorization before starting this homework. Please note that this homework uses the notation in my notes, with
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BENG0091 Coursework 1 To be submitted on Moodle by 25th Mar 2022. Please read the following guidelines before starting the work. Guidelines – You need to provide all MATLAB, Python or equivalent code that you have developed as part of your submission to Turnitin. This is compulsory. Include clarifications/comments in your code whenever you feel
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This assignment introduces the student to developing programs that implement computer vision and image analysis methods in Matlab, which will be tested on given images and video sequences. Students are to develop Matlab programs that implement computer vision and image analysis methods to solve a given task (details will be given here in due time).
Hardware and Software Optimisation Syllabus This module will cover the following • Choices to make when optimising • Hardware chip type specialisation • Compiler optimisations • Floating point to fixed point conversion 2 3 Optimisation Modifying a system to make it run more efficiently, or utilise fewer resources. Optimising hardware: ● use less energy, dissipate
Submission files You are expected to create a .mat file with the results and an .m file with the code as part of your submission. The .m file should be self-contained, i.e., I should be able to run the code on my computer loading the same data file without any error and ideally obtain what
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CIS 471/571(Fall 2020): Introduction to Artificial Intelligence Lecture 9: MDPs (Part 2) Thanh H. Nguyen Source: http://ai.berkeley.edu/home.html Announcement §Project 3: Reinforcement Learning § Deadline: Nov 10th, 2020 §Homework 3: MDPs and Reinforcement Learning § Will be posted tomorrow § Deadline: Nov 09th, 2020 Thanh H. Nguyen 10/28/20 2 Recap: MDPs §Markov decision processes: §States S
Naïve Bayes Classification AI lecture: Machine Learning Naïve Bayes Classification — Basic Machine Learning Model Material borrowed (and modified) from Jonathan Huang and I. H. Witten’s and E. Frank’s “Data Mining” and Jeremy Wyatt and others and revised by C.C. Hung * Outline Probability and Machine Learning Bayesian Classification Naïve Bayesian Classifier Examples Model parameters