EECS 3101 Winter 2019-20 – Exam Instructor: Jeff Edmonds 1) Gray a) Recursive i) path friend ii) code iii) proof 7 7 9 23 b) Iterative i) LI ii) Establish iii) Maintain iv) Post v) code 4 4 9 4 4 25 c) L(s,i,t) d) Bit(i) i) Proof ii) Code 9 5 4 18 2) […]
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CSC338. Homework 1 Due Date: Wednesday January 15, 9pm Please see the guidelines at https://www.cs.toronto.edu/~lczhang/338/homework.html What to Hand In Please hand in 2 files: • Python File containing all your code, named hw1.py. • PDF file named hw1_written.pdf containing your solutions to the written parts of the assignment. Your solution can be hand-written, but must
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Question 1. [8 marks] Circle either “True” or “False” for each of the below statements. 1. False 2. False 3. True 4. True 5. False 6. True 7. False 8. True We need a well-conditioned algorithm in order to obtain an accurate result. The truncation error is larger if we use a floating-point system with
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Introduction Project 4a Project 4a will be focusing on inference, using Bayesian networks and Particle Filtering. First you will be implementing a parser for a Bayesian network that calculates probabilities of assumptions given observations. Next you will use a dynamic Bayes’ Net to help pacman track ghosts using particle filtering. You will be modifying the
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Computing (2017) 99:1257–1270 DOI 10.1007/s00607-017-0562-9 Real-time uniprocessor scheduling with fewer preemptions Jinkyu Lee1 Received: 12 September 2016 / Accepted: 10 June 2017 / Published online: 28 June 2017 © Springer-Verlag GmbH Austria 2017 Abstract In this paper, we propose a simple, but effective scheduling framework for EDF and RM, which reduces the number of preemptions
Lecture Notes to Accompany Scientific Computing An Introductory Survey Second Edition by Michael T. Heath Chapter 3 Linear Least Squares Copyright ⃝c 2001. Reproduction permitted only for noncommercial, educational use in conjunction with the book. 1 Method of Least Squares Measurement errors inevitable in observational and experimental sciences Errors smoothed out by averaging over many
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Lecture Notes to Accompany Scientific Computing An Introductory Survey Second Edition by Michael T. Heath Chapter 2 Systems of Linear Equations Copyright ⃝c 2001. Reproduction permitted only for noncommercial, educational use in conjunction with the book. 1 Systems of Linear Equations Given m × n matrix A and m-vector b, find unknown n-vector x satisfying
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Lecture Notes to Accompany Scientific Computing An Introductory Survey Second Edition by Michael T. Heath Chapter 1 Scientific Computing Copyright ⃝c 2001. Reproduction permitted only for noncommercial, educational use in conjunction with the book. 1 Scientific Computing What is scientific computing? Design and analysis of algorithms for solving mathematical problems in science and engi- neering
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CSC338. Homework 9 Due Date: Wednesday March 25, 9pm Please see the guidelines at https://www.cs.toronto.edu/~lczhang/338/homework.html What to Hand In Please hand in 2 files: • Python File containing all your code, named hw9.py. • PDF file named hw9_written.pdf containing your solutions to the written parts of the assignment. Your solution can be hand-written, but must
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DEPARTMENT OF MATHEMATICAL AND COMPUTATIONAL SCIENCES UNIVERSITY OF TORONTO MISSISSAUGA Class Location & Time Instructor Office Location Office Hours E-mail Address Course Web Site Teaching Assistant Course Description CSC338H5S LEC0101 Numerical Methods Course Outline – Winter 2020 Wed, 03:00 PM – 05:00 PM IB 235 Lisa Zhang DH3078 lczhang [at] cs [dot] toronto [dot] edu