Анализ алгоритмов

Robert Sedgewick

Princeton University

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. In addition, this course covers generating functions and real asymptotics and then introduces the symbolic method in the context of applications in the analysis of algorithms and basic structures such as permutations, trees, strings, words, and mappings.

Analysis of Algorithms aims to enable precise quantitative predictions of the properties of large combinatorial structures. The theory has emerged over recent decades as essential both for the scientific analysis of algorithms in computer science and for the study of scientific models in many other disciplines, including probability theory, statistical physics, computational biology and information theory. This course covers recurrence relations, generating functions, asymptotics, and fundamental structures such as trees, permutations, strings, tries, words, and mappings, in the context of applications to the analysis of algorithms.


Course properties

Form of education
Informal
Formal education level
Undegraduate, Graduate
Recommended age for informal learning
16-18, 19-25, 25-45
Learning language
English
Discipline
Mathematics, Software and applications development and analysis, Information and Communication Technologies (ICTs)
Provider’s course code
CS_300
Course authors
Robert Sedgewick
Organization
Princeton University
Currency
USD
Course cost
0.0
Knowledge level entrance requirements
Math through calculus and basic familiarity with programming in a modern language such as Java. Knowledge of basic algorithms and data structures from Algorithms, Part I is helpful but not required.
Previous courses entrance requirements
Analytic Combinatorics Algorithms
Output knowledge, abilities, skills
The learner will know recurrence relations, generating functions, asymptotics, and fundamental structures such as trees, permutations, strings, tries, words, and mappings, in the context of applications to the analysis of algorithms.
Entrance test
Participants number limit
20000
Groups formation by readiness level
Teachers presence
Tutors presence
Facilitators presence
Training materials forms
texts, video lecture, presentation
Interactivity in training materials
Collaborative learning presence
Practical activities
project, labs
Discussions, forums presence
Webinars, video conferences presence
meetup presence
LMS integration
Learning Analytics
Certification presence
Certificate recognition
Princeton, Stanford
Course time limits
Course start date
2014-01-31
Course end date
2014-04-26
Opportunity to enter after start
Learning types (sync/async)
synhronous
Assessment types
test
Module unit
час
Course modules number
56
Tests (exams) number
2
Personal learning path possibility, course individualization
Operating System
Windows, Linux, MacOS
Supported browsers
Firefox, Chrome, IE
Learner’s devices
iPad, Laptop, Desktop
Peripherials
Mouse, Video-camera
Special needs support
Learning technologies
Mastery learning, Scaffolding

Comments