Survey of advanced algorithms and data structures such as heaps and heapsort, quicksort, red-black trees, B-trees, hash tables, graph algorithms, divide and conquer algorithms, dynamic programming, and greedy algorithms. Methods for proving correctness and asymptotic analysis.
DurationSurvey of advanced algorithms and data structures such as heaps and heapsort, quicksort, red-black trees, B-trees, hash tables, graph algorithms, divide and conquer algorithms, dynamic programming, and greedy algorithms. Methods for proving correctness and asymptotic analysis.
Mission Statement
Improve English language skills of students at the University of Minnesota, helping them develop cultural understanding and the communication, critical thinking, and academic skills needed to be successful in academic, professional, and social settings.
Help internationalize the University of Minnesota by providing native speakers of other languages access to the University, serving as a resource to University programs and faculty who work with multilingual students, and fostering greater understanding of linguistic and cultural diversity.
Support the ESL profession by offering training opportunities for ESL teachers, providing continued development for ESL professionals, and contributing to the field through presentations, publications, and collaboration on research in second-language teaching and learning.
Systematic study of data structures encountered in computing problems, methods of representing structured data, and techniques for operating on data structures.Â
The Data Structures & Algorithms course begins with a review of some important Java techniques and nuances in programming. The course requires some prior knowledge of Java and object-oriented programming, but not in data structures or algorithms.
Algorithms and data structures are the essential frameworks for solving almost any computer engineering problem.
In this course, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments.
Models, algorithms, recurrences, summations, growth rates. Probabilistic tools, upper and lower bounds; worst-case and average-case analysis, amortized analysis, dynamization. Comparison-based algorithms: search, selection, sorting, hashing. Information extraction algorithms (graphs, databases).
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