University of Colorado Boulder
Dynamic Programming, Greedy Algorithms
University of Colorado Boulder

Dynamic Programming, Greedy Algorithms

This course is part of Foundations of Data Structures and Algorithms Specialization

Taught in English

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Course

Gain insight into a topic and learn the fundamentals

4.5

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90%

Advanced level

Recommended experience

37 hours (approximately)
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Progress towards a degree

What you'll learn

  • Describe basic algorithm design techniques

  • Create divide and conquer, dynamic programming, and greedy algorithms

  • Understand intractable problems, P vs NP and the use of integer programming solvers to tackle some of these problems

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Assessments

17 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(86 reviews)

|

90%

Advanced level

Recommended experience

37 hours (approximately)
Flexible schedule
Learn at your own pace
Progress towards a degree

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This course is part of the Foundations of Data Structures and Algorithms Specialization
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There are 4 modules in this course

We will formally cover divide and conquer algorithms as a design scheme and look at some divide and conquer algorithms we have encountered in the past. We will learn some divide and conquer algorithms for Integer Multiplication (Karatsuba’s Algorithm), Matrix Multiplication (Strassen’s Algorithm), Fast Fourier Transforms (FFTs), and Finding Closest Pair of Points.

What's included

9 videos13 readings5 quizzes1 programming assignment1 discussion prompt

In this module, you will learn about dynamic programming as a design principle for algorithms. We will provide a step-by-step approach to formulating a problem as a dynamic program and solving these problems using memoization. We will cover dynamic programming for finding longest common subsequences, Knapsack problem and some interesting dynamic programming applications.

What's included

6 videos6 readings5 quizzes1 programming assignment

In this module, we will learn about greedy algorithms. We will understand the basic design principles for greedy algorithms and learn about a few algorithms for greedy scheduling and Huffman codes. We will also learn some interesting cases when being greedy provides a guaranteed approximation to the actual solution.

What's included

5 videos4 readings3 quizzes1 programming assignment

P vs NP, Examples such as Travelling Salesperson Problem, Vertex Cover, 3-Coloring and others; Integer Linear Programming and Translating Problems into Integer Programming.

What's included

9 videos5 readings4 quizzes1 programming assignment

Instructor

Instructor ratings
4.5 (29 ratings)
Sriram Sankaranarayanan
University of Colorado Boulder
5 Courses52,741 learners

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