Search result for Run time analysis of algorithms Online Courses & Certifications
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Approximation Algorithms
by Mark de Berg- 4.7
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Approx. 15 hours to complete
In order to successfully take this course, you should already have a basic knowledge of algorithms and mathematics. - Graph terminology, representations of graphs (adjacency lists and adjacency matrix), basic graph algorithms (BFS, DFS, topological sort, shortest paths) Analysis of the greedy-algorithm Analysis of the PTAS for knapsack: approximation ratio Analysis of the PTAS for knapsack: running time...
Algorithmic Thinking (Part 1)
by Luay Nakhleh , Scott Rixner , Joe Warren- 4.7
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Approx. 12 hours to complete
As the central part of the course, students will implement several important graph algorithms in Python and then use these algorithms to analyze two large real-world data sets. Efficiency of brute force distance Number of steps of brute force distance BFS running time - loose analysis BFS running time - tighter analysis...
Divide and Conquer, Sorting and Searching, and Randomized Algorithms
by Tim Roughgarden- 4.8
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Approx. 17 hours to complete
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). Guiding Principles for Analysis of Algorithms Interpretation of the 3 Cases Analysis of Contraction Algorithm...
Algorithms for Searching, Sorting, and Indexing
by Sriram Sankaranarayanan- 4.6
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Approx. 34 hours to complete
This course covers basics of algorithm design and analysis, as well as algorithms for sorting arrays, data structures such as priority queues, hash functions, and applications such as Bloom filters. Algorithms for Searching, Sorting, and Indexing can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform....
Machine Learning: Unsupervised Learning
by Charles Isbell , Michael Littman , Pushkar Kolhe- 0.0
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Approx. 1 months
Usefulness lesson 4 Feature Transformation Feature Transformation Words like Tesla Principal Components Analysis Independent Components Analysis Cocktail Party Problem Matrix Alternatives lesson 5 Information Theory History -Sending a Message Expected size of the message Information between two variables Mutual information Two Independent Coins Two Dependent Coins Kullback Leibler Divergence lesson 6 Unsupervised Learning Project...
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Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
by Tim Roughgarden- 4.8
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Approx. 14 hours to complete
The primary topics in this part of the specialization are: shortest paths (Bellman-Ford, Floyd-Warshall, Johnson), NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems (analysis of heuristics, local search). Analysis of a Greedy Knapsack Heuristic I Analysis of a Greedy Knapsack Heuristic II Analysis of Papadimitriou's Algorithm...
Geometric Algorithms
by Kevin Buchin- 0.0
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Approx. 18 hours to complete
This course deals with the algorithmic aspects of these tasks: we study techniques and concepts needed for the design and analysis of geometric algorithms and data structures. In order to successfully take this course, you should already have a basic knowledge of algorithms and mathematics. - O-notation, Ω-notation, Θ-notation; how to analyze algorithms...
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Introduction to Automated Analysis
by Mike , Kevin Wendt- 4.1
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Approx. 19 hours to complete
The learner will become familiar with the fundamental theory and applications of such approaches, and apply a variety of automated analysis techniques on example programs. for analysis and testing of software This knowledge would benefit several typical roles: Software Engineer, Software Engineer in Test, Test Automation Engineer, DevOps Engineer, Software Developer, Programmer, Computer Enthusiast....
I/O-efficient algorithms
by Mark de Berg- 4.6
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Approx. 10 hours to complete
In order to successfully take this course, you should already have a basic knowledge of algorithms and mathematics. - Graph terminology, representations of graphs (adjacency lists and adjacency matrix), basic graph algorithms (BFS, DFS, topological sort, shortest paths) A list of these mistakes can be found under resources. Why I/O-efficient Algorithms...
Machine Learning for Accounting with Python
by Linden Lu- 0.0
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Approx. 63 hours to complete
This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. 3 Introduction to Machine Learning Algorithms MODULE 2: FUNDAMENTAL ALGORITHMS I MODULE 3: Fundamental Algorithms II MODULE 6: INTRODUCTION TO TEXT ANALYSIS...
