算法代写代考包括以下内容：
Algorithms courses typically include topics such as data structures, basic algorithms, graph algorithms, dynamic programming, computational geometry, and number theory. They can also include more advanced topics such as parallel algorithms, randomized algorithms, and approximation algorithms.
Assignment 5 – Expectation Maximization Expectation Maximization – Assignment 5 – CS6601 Jupyter Notebook: You will be using jupyter notebook to complete this assignment. To open the jupyter notebook, navigate to your assignment folder, activate the conda environment conda activate ai_env , and run jupyter notebook . Project description and all of the functions that […]
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CS6601 Assignment 5 submission Assignment 5 – Expectation Maximization¶ Automatic image processing is a key component to many AI systems, including facial recognition and video compression, instance segmentation of images and point cloud data. One basic method for processing is segmentation, by which we divide an image into a fixed number of components in order
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COMP2017 / COMP9017 Tutorial 5 File IO, Function Pointers and Signals File IO and Function Pointers As per the unix philosophy, “Everything is a file”, this means that we can typically get majority of our information from unix processes, pipes and memory mapped files. Processes running inside UNIX have a table set aside for keeping
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Comp3620/Comp6320 Artificial Intelligence Tutorial 1: Search Formulations, Strategies, and Algorithms March 14 – 20, 2023 Exercise 1 (problem formulation) For each of the following problem, explain how states and actions can be represented, and give the initial state, goal test, successor function, and a plausible step cost function. Remember from the lectures that these elements
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COMP9417 – Machine Learning Homework 2: Kernel Features & Model Combinations Introduction In this homework we first take a closer look at feature maps induced by kernels. We then ex- plore a creative use of the gradient descent method introduced in homework 1. We will show that gradient descent techniques can be used to construct
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Machine Problem 4 Tiled Matrix Multiplication The objective of this machine problem is to get you familiar with using shared memory to write optimized kernel algorithms by implementing a “tiled” version of matrix multiplication. A matrix multiplication takes two input matrices M and N and produces an output matrix P. For simplicity, we will only
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NLP tweets In this homework, you’ll be working with a collection of tweets. The task is to predict the geolocation (country) where the tweet comes from. This homework involves writing code to preprocess data and perform text classification. Preprocessing (4 marks)¶ Instructions: Download the data (as1-data.json) from Canvas and put it in the same directory
Template Matching Template Matching with Cache Friendly Code http://pxlcon.jimmysomething.com In this assignment, you will use template matching to find Waldo in a pixel art image. Waldo is easily recognizable because of his glasses and his red and white striped shirt and hat. Template matching is the simplest of a family of algorithms that are used
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Assignment COMP0046 – Networks and systemic risk Please download the data in the folder “Coursework data”. The data set contains information about balance sheets of 145 banks. A matrix of interbank exposures is provided in file “interbankExposures.csv”. Entry (i,j) of such matrix represents the exposure of i towards j. File “bankAssetWeightedNetwork.csv” contains a matrix that
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We want to build a queue for integer elements that in addition to the operations we saw during the lecture, also supports a SEESAW operation that on a given queue holding the elements returns All operations should run in O(1) time. Your data structure should take O(n) space, where n is the number of elements
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