module Main where import Test.HUnit import Prelude hiding (all, concat, reverse, takeWhile, zip, (++)) import Text.ParserCombinators.ReadP (count) import Data.Bits (Bits(xor)) todo = error “It is your job to fill in this todo” studentName :: String studentName = todo testName :: Test testName = “testName” ~: assertBool “You need to provide a `studentName`” (not (null studentName)) […]
UBC CPSC310-22W2: Intro to SE – Checkpoint 1 Project Grading Checkpoint 0 Checkpoint 1 UBC CPSC310-22W2: Intro to SE Checkpoint 1 Learning Outcomes Can model different data (a dataset , a query) into suitable data structures and reason about alternative representations. Can understand and work with external libraries (fs-extra, JSZip) by following documentation. Able to
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EECE5644 Fall 2018 – Exam 2 This assignment is due on Blackboard by 10:00am ET on Wednesday, December 5, 2018. Please submit your solutions on Blackboard in a single PDF file that includes all math, visual and quantitative results (plots, tables, etc), as well as your code (appended after your answers/solutions for each question). Do
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EECE5644 Introduction to Machine Learning and Pattern Recognition HW3 Question 1 (60%) In this exercise, you will train many multilayer perceptrons (MLPs) to approximate class label posteriors. The MLPs will be trained using maximum likelihood parameter estimation (or equiva- lently, minimum average cross-entropy loss). You will then use the trained models to approximate a maximum
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EECE5644 Assignment 4 Question 1 (60%) Train and test Support Vector Machine (SVM) and Multi-layer Perceptron (MLP) classifiers that aim for minimum probability of classification error, i.e., we are using 0-1 loss; all error in- stances are equally bad. You may use a trusted implementation in your choice of programming language and software packages, e.g.,
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EECE5644 Assignment 0 Question 1 (30%) Design a classifier that achieves minimum probability of error for a three-class problem where the class priors are respectively p(L = 1) = 0.15, p(L = 2) = 0.35, p(L = 3) = 0.5 and the class- conditional data distributions are all Gaussians for two-dimensional data vectors: −1
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EECE5644 Introduction to Machine Learning and Pattern Recognition Assignment 2 Question 1 (50%) The probability density function (pdf) for a 2-dimensional real-valued random vector X is as follows: p(x) = p(L = 0)p(x|L = 0)+ p(L = 1)p(x|L = 1). Here L is the true class label that indicates which class-conditioned pdf generates the data.
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Computer Graphics, Autumn 2019 Assignment 2 The Path of Light Alexandros Keros, Kartic Subr Due date: 04/11/19 (5pm) Your recent success with the “Reel to Real” Studios interview has landed you a position as a computer graphics intern! Your first task is to implement a custom ray tracer, in C++, to augment the company’s rendering
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EECE5644 Spring 2023 – Assignment 1 Submit: Friday, 2023-February-17 before 08:00 ET (upload PDF to Canvas) Please submit your solutions in a single PDF file using the Canvas/Assignments page. The PDF should includes all math, numerical and visual results. Code can be appended in the PDF or a link to your online code repository can
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import sys WRITE YOUR CODE BELOW. from numpy import zeros, float32 import pgmpy from pgmpy.models import BayesianModel from pgmpy.factors.discrete import TabularCPD from pgmpy.inference import VariableElimination #You are not allowed to use following set of modules from ‘pgmpy’ Library. # pgmpy.sampling.* # pgmpy.factors.* # pgmpy.estimators.* def make_security_system_net(): “””Create a Bayes Net representation of the above security
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