Brief Introduction
The design and analysis of algorithms form an essential basis for computer science. This course is useful for those who want to pursue advanced studies in computer science, as well as those who want to work as a software engineer.Course Summary
This course provides an introduction to graduate-level algorithms, including algorithmic design and analysis, graph algorithms, and randomized algorithms.Key Learning Points
- Learn about the design and analysis of algorithms
- Understand graph algorithms and randomized algorithms
- Explore real-world applications of algorithms
Job Positions & Salaries of people who have taken this course might have
- Software Engineer
- USA: $95,000
- India: ₹6,50,000
- Spain: €36,000
- Data Scientist
- USA: $113,000
- India: ₹9,00,000
- Spain: €40,000
- Algorithm Engineer
- USA: $120,000
- India: ₹12,00,000
- Spain: €50,000
Related Topics for further study
Learning Outcomes
- Understand basic concepts of algorithm design and analysis
- Learn to apply graph algorithms and randomized algorithms
- Develop critical thinking and problem-solving skills
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of data structures and programming concepts
- Familiarity with mathematical concepts like calculus and probability theory
Course Difficulty Level
IntermediateCourse Format
- Self-paced
- Online
Similar Courses
- Algorithms, Part I
- Data Structures and Algorithms Specialization
- Algorithmic Toolbox
Related Education Paths
Notable People in This Field
- Donald Knuth
- Jon Kleinberg
Related Books
Description
Learn advanced techniques for designing algorithms and apply them to hard computational problems.Requirements
- Students are expected to have an undergraduate course on the design and analysis of algorithms. In particular, they should be familiar with basic graph algorithms, including DFS, BFS, and Dijkstra's shortest path algorithm, and basic dynamic programming and divide and conquer algorithms (including solving recurrences). An undergraduate course in discrete mathematics is assumed, and students should be comfortable analyzing the asymptotic running time of algorithms. The course uses the textbook Algorithms by Sanjoy Dasgupta, Christos Papadimitriou, and Umesh Vazirani. See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Outline
- lesson 1 Dynamic Programming Fibonacci Numbers Longest Increasing Subsequence (LIS) Longest Common Subsequence (LCS) Knapsack Chain Matrix Multiplication Shortest Path Algorithms lesson 2 Randomized Algorithms Modular Arithmetic: Fast Modular Exponentiation Multiplicative Inverses RSA Cryptosystem: Fermat's Little Theorem RSA Protocol Primality Testing Hashing: Traditional Chain Hashing Bloom Filters lesson 3 Divide and Conquer Fast Integer Multiplication Linear-Time Median Fast Fourier Transform lesson 4 Graph Algorithms Strongly Connected Components 2-Satisfiability Minimum Spanning Tree Markov Chains PageRank lesson 5 Max-Flow Problems Ford-Fulkerson Algorithm Max-Flow Min-Cut Theorem Edmonds-Karp Algorithm Max-Flow applied to Image Segmentation lesson 6 Linear Programming Simplex Algorithm Weak and Strong Duality Max-SAT Approximation lesson 7 NP-Completeness Complexity Classes: P NP NP-Complete NP-Complete Problems: 3-SAT Independent Set Clique Vertex Cover Knapsack Subset-Sum Halting Problem
Summary of User Reviews
Discover a comprehensive guide to graduate-level algorithms with Udacity's Introduction to Graduate Algorithms. This course is highly recommended by users who enjoyed its challenging yet rewarding nature.Pros from User Reviews
- Excellent content and comprehensive coverage of algorithms
- Engaging lectures and challenging assignments
- Great support from instructors and community
- Flexible schedule and self-paced learning
- Useful resources and practical applications
Cons from User Reviews
- Some users found the course too advanced for beginners
- The course can be time-consuming and requires a significant commitment
- The course may not be suitable for those who prefer a more structured learning environment
- Some users found the explanations and examples to be unclear or confusing
- The course may require additional resources or background knowledge for some learners
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