E.g., Wednesday, January 28, 2015
E.g., Wednesday, January 28, 2015
E.g., Wednesday, January 28, 2015
Self Paced

This course focuses on the fundamentals of computer algorithms, emphasizing methods useful in practice. We look into the algorithm analysis as a way to understand behavior of computer programs as a function of its input size.

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Self Paced

The advent of computers transformed science. Large, complicated datasets that once took researchers years to manually analyze could suddenly be analyzed within a week using computer software. Computational biology refers to the use of computers to automate data analysis or model hypotheses in the field of biology. This course will prepare students in all subfields of biology for future research and data analysis opportunities. Computer science students interested in biological applications will also find it useful.

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Self Paced Course - Start anytime

Can we program machines to learn like humans? This Reinforcement Learning course will teach you the algorithms for designing self-learning agents like us!

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The main goal of this corse is to provide the student with the fundamentals on computer architecture and to introduce the C.

10
Average: 10 (1 vote)
Self Paced Course - Start anytime

The purpose of this course is to introduce students to the topics of data structures and algorithm design along with their respective applications.

10
Average: 10 (1 vote)
Self Paced Course - Start anytime

In this course you will examine real world problems -- rescue the Apollo 13 astronauts, stop the spread of epidemics, and fight forest fires -- involving differential equations and figure out how to solve them using numerical methods.

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Ever played the Kevin Bacon game? This class will show you how it works by giving you an introduction to the design and analysis of algorithms, enabling you to discover how individuals are connected.

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Cryptography is essentially the science of writing in secret code. In data and telecommunications, cryptography has specific security requirements, such as authentication, privacy or confidentiality, integrity, and non-repudiation. To meet these security requirements, we employ secret key (or symmetric) cryptography, public-key (or asymmetric) cryptography, and hash functions.

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Feb 16th 2015

This is the second course in a two-part series on bioinformatics algorithms, covering the following topics: evolutionary tree reconstruction, applications of combinatorial pattern matching for read mapping, gene regulatory analysis, protein classification, computational proteomics, and computational aspects of human genetics.

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Feb 16th 2015

This course will cover algorithms for solving various biological problems along with a handful of programming challenges testing your ability to implement these algorithms. It offers a gentler-paced alternative to the instructors' two other courses, Bioinformatics Algorithms (Part 1 and Part 2).

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Jan 19th 2015

Dieser Kurs vermittelt einen Überblick über die Grundlagen des Maschinellen Sehens an Hand der Extraktion von 3D-Information aus dem Stereokamerabild einer Szene.

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Jan 31st 2015

This class teaches algorithms for extracting models and other information from very large amounts of data. The emphasis is on techniques that are efficient and that scale well.

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Jan 19th 2015

In this course you will learn several fundamental principles of algorithm design: divide-and-conquer methods, graph algorithms, practical data structures, randomized algorithms, and more.

9
Average: 9 (1 vote)
Jan 23rd 2015

This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations. Part I covers basic iterable data types, sorting, and searching algorithms.

8
Average: 8 (1 vote)
Feb 2nd 2015

Learn core distributed computing concepts that underlie today’s and tomorrow’s cloud computing systems.

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Jan 23rd 2015

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.

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Feb 2nd 2015

Learn the basic components of building and applying prediction functions with an emphasis on practical applications.

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Jan 12th 2015

The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.

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