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Dynamic Programming, Greedy Algorithms
  • Level Expert
  • المدة 38 ساعات hours
  • الطبع بواسطة University of Colorado Boulder
  • Offered by

عن

This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems. We will also cover some advanced topics in data structures. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder

الوحدات

Course Overview

    • 5 Readings
    • 1 Discussion
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1 Discussions

  • Introduce Yourself!

5 Readings

  • Earn Academic Credit for Your Work!
  • Course Support
  • Reading: Important Prerequisites
  • Logistics: Textbook and Readings
  • Important Specialization Information

Overview of Module 1

    • 1 Readings
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1 Readings

  • Overview of Module 1

What Are Divide and Conquer Algorithms?

    • 1 Videos
    • 1 Readings
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1 Videos

  • What Are Divide and Conquer Algorithms?

1 Readings

  • CLRS Chapter 4

Max Subarray Problem Using Divide and Conquer

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Max Subarray Problem Using Divide and Conquer

1 Readings

  • CLRS Chapter 4.1

1 Quiz

  • Max Subarray Problem

Karatsuba’s Multiplication Algorithm

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Karatsuba’s Multiplication Algorithm

1 Readings

  • Jupyter Notebook on Karatsuba's Algorithm

1 Quiz

  • Karatsuba's Multiplication Algorithm

Master Method Revisited

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Master Method Revisited

1 Readings

  • CLRS Chapters 4.3 - 4.5

1 Quiz

  • Master Method

Fast Fourier Transform Algorithm

    • 5 Videos
    • 3 Readings
    • 2 Quiz
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5 Videos

  • FFT Part 1: Introduction and Complex Numbers
  • FFT Part 2: Definition and Interpretation of Discrete Fourier Transforms
  • FFT Part 3: Divide and Conquer Algorithm for FFT
  • Application # 1 : Fast Polynomial Multiplication using FFT
  • Application # 2: Data Analysis using FFT

3 Readings

  • Basics of Complex Numbers
  • Fourier Transforms
  • Fast Fourier Transform

2 Quiz

  • Complex Numbers and Roots of Unity
  • FFT Algorithm and Applications

Problem Set

    • 1 Programming
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1 Programming

  • Problem Set 1

Overview of Module 2

    • 1 Readings
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1 Readings

  • Overview of Module 2

Introduction to Dynamic Programming + Rod Cutting Problem

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Introduction to Dynamic Programming + Rod Cutting Problem

1 Readings

  • Rod Cutting Problem

1 Quiz

  • Rod Cutting Problem and Recurrence

Rod Cutting Problem: Memoization

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Rod Cutting Problem: Memoization

1 Readings

  • Memoization

1 Quiz

  • Memoization

Coin Changing Problem

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Coin Changing Problem

1 Readings

  • Coin Changing Problem

1 Quiz

  • Coinchanging Problem

Knapsack Problem

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Knapsack Problem

1 Readings

  • Jupyter Notebook

1 Quiz

  • Knapsack Problem

When Optimal Substructure Fails

    • 1 Videos
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1 Videos

  • When Optimal Substructure Fails

Dynamic Programming: Longest Common Subsequence

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Dynamic Programming: Longest Common Subsequence

1 Readings

  • CLRS Chapter 15.4

1 Quiz

  • Longest Common Subsequence

Problem Set

    • 1 Programming
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1 Programming

  • Problem Set 2

Overview of Module 3

    • 1 Readings
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1 Readings

  • Overview of Module 3

Introduction to Greedy Algorithms

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Introduction to Greedy Algorithms

1 Readings

  • CLRS Chapter 16

1 Quiz

  • Greedy Algorithms

Greedy Interval Scheduling

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Greedy Interval Scheduling

1 Readings

  • CLRS Chapters 16.1 and 16.2

1 Quiz

  • Greedy Interval Scheduling

Prefix Codes and Optimal Prefix Codes

    • 3 Videos
    • 1 Readings
    • 1 Quiz
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3 Videos

  • Prefix Codes
  • Huffman Codes
  • Huffman Codes: Proof of Optimality

1 Readings

  • CLRS Chapter 16.3

1 Quiz

  • Huffman Codes

Problem Set

    • 1 Programming
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1 Programming

  • Problem Set 3

Overview of Module 4

    • 1 Readings
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1 Readings

  • Overview of Module 4

Decision Problems and Languages

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Decision Problems and Languages

1 Readings

  • CLRS Chapter 34

1 Quiz

  • Decision Problems and Languages

Polynomial Time Problems

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • Polynomial Time Problems

1 Readings

  • CLRS chapter 34.1

1 Quiz

  • Polynomial Time and Certificates

NP Definition and NP Completeness

    • 2 Videos
    • 1 Readings
    • 1 Quiz
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2 Videos

  • NP Definition
  • NP Completeness and Reductions

1 Readings

  • CLRS Chapter 34.2 and 34.3

1 Quiz

  • NP Completeness Reductions

NP Complete Problems

    • 1 Videos
    • 1 Readings
    • 1 Quiz
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1 Videos

  • NP Complete Problems: Examples

1 Readings

  • CLRS Chapter 34.5

1 Quiz

  • NP Completeness Problems

Supplementary Lectures: Quantum Computing

    • 4 Videos
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4 Videos

  • Computation and Physics
  • Qubits and Operations
  • Bell's Inequality
  • Grover's Search Algorithm

Programming Assignment

    • 1 Programming
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1 Programming

  • Problem Set 4

Auto Summary

Explore key algorithm design techniques with Coursera's "Dynamic Programming, Greedy Algorithms" course, focusing on divide and conquer, dynamic programming, and greedy methods. Ideal for data science and AI enthusiasts, this expert-level course, instructed by CU Boulder, also delves into NP-completeness and optimization using linear/integer programming solvers. Offered as part of CU Boulder’s MS in Data Science or Computer Science on Coursera, it features short 8-week sessions, pay-as-you-go tuition, and performance-based admission, perfect for recent graduates and working professionals.

Sriram Sankaranarayanan
Instructor

Sriram Sankaranarayanan