The primary topics in this part of the specialization are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), and randomized algorithms (QuickSort, contraction algorithm for min cuts).
이 강좌는 알고리즘 전문 분야의 일부입니다.
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
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About this Course
최근 조회 207,391회
Course을(를) 수강하는 학습자
- Machine Learning Engineers
- Software Engineers
- Data Scientists
- Technical Leads
- Data Engineers
귀하가 습득할 기술
AlgorithmsRandomized AlgorithmSorting AlgorithmDivide And Conquer Algorithms
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 3시간 필요
Week 1
Introduction; "big-oh" notation and asymptotic analysis.
13개 동영상 (총 130분), 3 readings, 2 quizzes
13개의 동영상
Integer Multiplication8m
Karatsuba Multiplication12m
About the Course17m
Merge Sort: Motivation and Example8m
Merge Sort: Pseudocode12m
Merge Sort: Analysis9m
Guiding Principles for Analysis of Algorithms15m
The Gist14m
Big-Oh Notation4m
Basic Examples7m
Big Omega and Theta7m
Additional Examples [Review - Optional]7m
3개의 읽기 자료
Welcome and Week 1 Overview10m
Overview, Resources, and Policies10m
Lecture slides10m
2개 연습문제
Problem Set #110m
Programming Assignment #12m
주
2완료하는 데 3시간 필요
Week 2
Divide-and-conquer basics; the master method for analyzing divide and conquer algorithms.
11개 동영상 (총 170분), 2 readings, 2 quizzes
11개의 동영상
O(n log n) Algorithm for Counting Inversions II16m
Strassen's Subcubic Matrix Multiplication Algorithm22m
O(n log n) Algorithm for Closest Pair I [Advanced - Optional]31m
O(n log n) Algorithm for Closest Pair II [Advanced - Optional]18m
Motivation7m
Formal Statement10m
Examples13m
Proof I9m
Interpretation of the 3 Cases10m
Proof II16m
2개의 읽기 자료
Week 2 Overview10m
Optional Theory Problems (Batch #1)10m
2개 연습문제
Problem Set #210m
Programming Assignment #22m
주
3완료하는 데 3시간 필요
Week 3
The QuickSort algorithm and its analysis; probability review.
9개 동영상 (총 156분), 1 reading, 2 quizzes
9개의 동영상
Partitioning Around a Pivot24m
Correctness of Quicksort [Review - Optional]10m
Choosing a Good Pivot22m
Analysis I: A Decomposition Principle21m
Analysis II: The Key Insight11m
Analysis III: Final Calculations8m
Probability Review I25m
Probability Review II17m
1개의 읽기 자료
Week 3 Overview10m
2개 연습문제
Problem Set #310m
Programming Assignment #36m
주
4완료하는 데 4시간 필요
Week 4
Linear-time selection; graphs, cuts, and the contraction algorithm.
11개 동영상 (총 184분), 3 readings, 3 quizzes
11개의 동영상
Randomized Selection - Analysis20m
Deterministic Selection - Algorithm [Advanced - Optional]16m
Deterministic Selection - Analysis I [Advanced - Optional]22m
Deterministic Selection - Analysis II [Advanced - Optional]12m
Omega(n log n) Lower Bound for Comparison-Based Sorting [Advanced - Optional]13m
Graphs and Minimum Cuts15m
Graph Representations14m
Random Contraction Algorithm8m
Analysis of Contraction Algorithm30m
Counting Minimum Cuts7m
3개의 읽기 자료
Week 4 Overview10m
Optional Theory Problems (Batch #2)10m
Info and FAQ for final exam10m
3개 연습문제
Problem Set #410m
Programming Assignment #42m
Final Exam20m
4.8
529개의 리뷰35%
이 강좌를 수료한 후 새로운 경력 시작하기
38%
이 강좌를 통해 확실한 경력상 이점 얻기
29%
급여 인상 또는 승진하기
Divide and Conquer, Sorting and Searching, and Randomized Algorithms의 최상위 리뷰
Well researched. Topics covered well, with walkthrough for exam.le cases for each new introduced algorithm. Great experience, learned a lot of important algorithms and algorithmic thinking practices.
Very good course in algorithms. I bought the book to help me understand but the lectures make it way easier and thus much more fun to understand the analysis. Looking forward to complete the spec
강사
Tim Roughgarden
ProfessorComputer Science
스탠퍼드 대학교 정보
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
알고리즘 전문 분야 정보
Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth. This specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.
About the instructor: Tim Roughgarden has been a professor in the Computer Science Department at Stanford University since 2004. He has taught and published extensively on the subject of algorithms and their applications.
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