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Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

The primary topics in this part of the specialization are: greedy algorithms (scheduling, minimum spanning trees, clustering, Huffman codes) and dynamic programming (knapsack, sequence alignment, optimal search trees).

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What's inside

Syllabus

Week 1

Two motivating applications; selected review; introduction to greedy algorithms; a scheduling application; Prim's MST algorithm.

Week 2

Kruskal's MST algorithm and applications to clustering; advanced union-find (optional).

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Provides a solid foundation in greedy algorithms and dynamic programming

Taught by Tim Roughgarden, a recognized expert in algorithm design and analysis

Covers fundamental concepts used in computer science and software engineering

Requires a strong understanding of mathematics and programming

May require additional background reading or external resources for a deeper understanding of advanced topics

Students with no prior experience in algorithm design may find the pace challenging

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Reviews summary

Algorithms: greedy, mst, dp - challenging assignments

According to learners, this course on greedy algorithms, minimum spanning trees, and dynamic programming is largely positive, offering a solid foundation in core algorithms. Many highlight the clarity of the lectures and the instructor's ability to explain complex topics. However, the programming assignments are frequently cited as very challenging, sometimes requiring significant time and effort, or even external resources. Despite the difficulty, many students find the assignments extremely valuable and rewarding for solidifying understanding. The Dynamic Programming section, in particular, is mentioned as having a notable jump in assignment difficulty. While demanding, the course is seen as a rigorous and worthwhile learning experience.

May require strong prior CS background.

"Prerequisites felt a bit higher than stated, definitely need a good grasp of data structures and some proofs."

"Not suitable for beginners in algorithms. Requires prior knowledge."

Provides strong grounding in algorithms.

"Highly recommend for anyone wanting a solid foundation in algorithms."

"Solid course on important algorithms. Good for reinforcing concepts if you already have a base."

"This course provided a rigorous introduction to these algorithms."

Programming problems are rigorous and valuable.

"The programming assignments were challenging but very rewarding, truly helping to solidify the concepts."

"The assignments, though tough, are the best part. They make you really think and implement the algorithms yourself. This is crucial for understanding."

"Highly recommend! This course provided a rigorous introduction to these algorithms. The assignments were challenging but pushed me to learn."

"Programming assignments are hard but valuable. Be prepared to spend a lot of time on them."

Explanations are easy to follow and grasp.

"The lectures are very clear and break down complex topics like dynamic programming into understandable parts."

"Fantastic explanation of core algorithms. Lectures are top-notch."

"One of the best algorithms courses I've taken online. The instructor is superb, explaining complex ideas clearly."

"Covers important topics well. The lectures are clear."

DP section and assignments are particularly hard.

"Dynamic programming felt a bit rushed towards the end, and the final programming problem was quite difficult."

"DP section was okay, but the difficulty jumped significantly with the programming assignment."

"The DP problem was a huge leap in difficulty from the previous assignments."

The course, especially assignments, is demanding.

"Found this course very challenging. The programming assignments are extremely difficult and require a lot of prior knowledge or external help."

"The theory is explained well, but the gap between theory and the difficulty of the programming assignments is large. Needed external resources..."

"Expected more guidance on implementation. Not suitable for beginners in algorithms."

"Good course, but demanding. Be prepared to spend a lot of time."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming with these activities:

Organize course materials

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Improve understanding and retention by organizing course materials systematically.

Browse courses on Greedy Algorithms

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Gather lecture notes, slides, and assignments.
Create a structured system for organizing the materials.

Review 'Introduction to Algorithms' by Cormen, Leiserson, Rivest, and Stein

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Strengthen the theoretical understanding of greedy algorithms and dynamic programming by reviewing a foundational textbook.

View Introduction to Algorithms, fourth edition on Amazon

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Read the relevant chapters on greedy algorithms and dynamic programming.
Solve practice problems from the book.

Participate in peer study sessions

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Enhance understanding and problem-solving abilities by collaborating with peers.

Browse courses on Greedy Algorithms

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Find a group of classmates with similar interests.
Set regular meeting times and discuss course material.
Work together on practice problems or projects.

Three other activities

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Solve practice problems on algorithmic thinking

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Improve algorithmic thinking and problem-solving skills by solving practice problems related to greedy algorithms and dynamic programming.

Browse courses on Greedy Algorithms

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Identify the main concepts of greedy algorithms and dynamic programming.
Solve problems from online resources or textbooks.
Analyze the solutions and identify patterns.

Explain a greedy algorithm or dynamic programming technique

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Deepen understanding of a greedy algorithm or dynamic programming technique by explaining it to others.

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Choose a specific greedy algorithm or dynamic programming technique.
Research and understand the algorithm thoroughly.
Create a presentation, video, or written explanation.
Share your explanation with others and seek feedback.

Develop a mobile app to solve scheduling problems

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Apply greedy algorithms to a practical problem by developing a mobile app for solving scheduling problems.

Browse courses on Greedy Algorithms

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Identify specific scheduling problems to target.
Design and implement a greedy algorithm for solving the problems.
Develop a mobile app that incorporates the greedy algorithm.
Test and refine the app based on user feedback.

Career center

Learners who complete Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming will develop knowledge and skills that may be useful to these careers:

Operations Research Analyst

An Operations Research Analyst uses mathematical models and algorithms to improve the efficiency of business processes. The course's focus on greedy algorithms and dynamic programming can help an Operations Research Analyst develop the skills needed to solve complex optimization problems.

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Computer Scientist

A Computer Scientist designs, develops, and implements computer software and systems. The course's focus on greedy algorithms and dynamic programming can help a Computer Scientist develop the skills needed to design and implement efficient algorithms.

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Software Engineer

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Data Scientist

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Financial Analyst

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Management Consultant

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Actuary

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Statistician

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Operations Manager

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Project Manager

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Business Analyst

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Systems Analyst

A Systems Analyst designs and implements computer systems. The course's focus on greedy algorithms and dynamic programming can help a Systems Analyst develop the skills needed to design and implement efficient systems.

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Database Administrator

A Database Administrator manages and maintains databases. The course's focus on greedy algorithms and dynamic programming can help a Database Administrator develop the skills needed to optimize database performance.

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Network Administrator

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Information Security Analyst

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Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming.

Introduction to Algorithms, fourth edition

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This classic textbook covers greedy algorithms, minimum spanning trees, and dynamic programming in great detail. It valuable reference for anyone interested in learning more about these topics.

Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Algorithms: greedy, mst, dp - challenging assignments

Activities

Career center

Reading list

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