Algorithms Study guides, Class notes & Summaries

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OCR A Level Computer Science H446/02 MARK SCHEME 2023: Algorithms and programming
  • OCR A Level Computer Science H446/02 MARK SCHEME 2023: Algorithms and programming

  • Exam (elaborations) • 36 pages • 2023
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OCR AS Level Computer Science H046/02 JUNE 2023 MARK SCHEME: Algorithms and problem solving
  • OCR AS Level Computer Science H046/02 JUNE 2023 MARK SCHEME: Algorithms and problem solving

  • Exam (elaborations) • 26 pages • 2023
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Data Structures and Algorithms I - C949 WGU With Correct Answers.
  • Data Structures and Algorithms I - C949 WGU With Correct Answers.

  • Exam (elaborations) • 11 pages • 2022
  • Algorithm efficiency typically measured by the algorithm's computational complexity Computational complexity the amount of resources used by the algorithm. The most common resources considered are the runtime and memory usage. runtime complexity a function, T(N), that represents the number of constant time operations performed by the algorithm on an input of size N Space-complexity (of an algorithm) a function, S(N), that represents the number of fixed-size memory units...
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CMPUT204: Introduction to Algorithms Final Exam
  • CMPUT204: Introduction to Algorithms Final Exam

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  • CMPUT204: Introduction to Algorithms Final Exam
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Business Intelligence, Analytics, and Data Science-A Managerial Perspective, 4th edition Ramesh, Dursun and Efraim  TEST BANK Business Intelligence, Analytics, and Data Science-A Managerial Perspective, 4th edition Ramesh, Dursun and Efraim  TEST BANK
  • Business Intelligence, Analytics, and Data Science-A Managerial Perspective, 4th edition Ramesh, Dursun and Efraim TEST BANK

  • Exam (elaborations) • 93 pages • 2023
  • TEST BANK for Business Intelligence, Analytics, and Data Science: A Managerial Perspective, 4th edition Ramesh Sharda, Dursun Delen and Efraim Turban. ISBN-13: 5711. An Overview of Business Intelligence, Analytics, and Data Science Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization Descriptive Analytics II: Business Intelligence and Data Warehousing Predictive Analytics I: Data Mining Process, Methods, and Algorithms Predictive Analytics II: Text, Web, and Social Me...
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Summary of the course data structures and algorithms for AI, second year course bachelor AI
  • Summary of the course data structures and algorithms for AI, second year course bachelor AI

  • Summary • 56 pages • 2022
  • This summary captures the course data structures and algorithms for AI. Do note that this is the AI version of the course! The Computer Science version (or any other possible version of the course) might be different. It captures all video's used as lectures in the year 2020-2021 (COVID breakout).
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CSE 551: Foundations of Algorithms - Arizona State University. CSE 551 Design and Analysis of Algorithms Week 3 Unit 4 Quiz Solutions, April 6, 2020. Problem, Solutions and Rationale.
  • CSE 551: Foundations of Algorithms - Arizona State University. CSE 551 Design and Analysis of Algorithms Week 3 Unit 4 Quiz Solutions, April 6, 2020. Problem, Solutions and Rationale.

  • Exam (elaborations) • 7 pages • 2023
  • CSE 551: Foundations of Algorithms - Arizona State University. CSE 551 Design and Analysis of Algorithms Week 3 Unit 4 Quiz Solutions, April 6, 2020. Problem, Solutions and Rationale. CSE 551: Foundations of Algorithms - Arizona State University. CSE 551 Design and Analysis of Algorithms Quiz 4 Solutions, April 6, 2020. Problem, Solutions and Rationale. CSE 551: Quiz 4 Solutions J amison Weber April 6, 2020 1 Problem 1 Solve the following recurrence relation using any method. Provide your answer...
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KHAN ACADEMY ALGORITHMS LIST 2024 WITH COMPLETE SOLUTION
  • KHAN ACADEMY ALGORITHMS LIST 2024 WITH COMPLETE SOLUTION

  • Exam (elaborations) • 27 pages • 2024
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  • KHAN ACADEMY ALGORITHMS LIST 2024 WITH COMPLETE SOLUTION A statistician developed this procedure to calculate the "variance" of a list of numbers. The variance is a statistical quantity that corresponds to the average of the sum of the squared differences of each number from the mean. As input, the procedure takes a list of numbers and its mean: PROCEDURE calculateVariance(numbers, mean) { count ← 0 sumSquaredDiffs ← 0 FOR EACH num IN numbers { diff ← (num - mean) squaredD...
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