Courses tagged with "Mathematics" (32)

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Starts : 2009-09-01
16 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Graduate Mathematics MIT OpenCourseWare

The focus of the course is the concepts and techniques for solving the partial differential equations (PDE) that permeate various scientific disciplines. The emphasis is on nonlinear PDE. Applications include problems from fluid dynamics, electrical and mechanical engineering, materials science, quantum mechanics, etc.

Starts : 2015-02-01
14 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Graduate Mathematics MIT OpenCourseWare

This course is organized around algorithmic issues that arise in machine learning. Modern machine learning systems are often built on top of algorithms that do not have provable guarantees, and it is the subject of debate when and why they work. In this class, we focus on designing algorithms whose performance we can rigorously analyze for fundamental machine learning problems.

Starts : 2013-10-21
No votes
Coursera Free Closed [?] Computer Sciences English Mathematics

In this course we’ll explore complex analysis, complex dynamics, and some applications of these topics.

Starts : 2016-03-04
No votes
Coursera Free Closed [?] Computer Sciences English Computer Science Mathematics Theory

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.

Starts : 2013-02-08
99 votes
Coursera Free Closed [?] Computer Sciences Combinatorics Mathematics

This course teaches a calculus that enables precise quantitative predictions of large combinatorial structures. Part I 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.

Starts : 2015-09-15
No votes
Coursera Free Computer Sciences English Artificial Intelligence Biology & Life Sciences Computer Science Mathematics Medicine Statistics and Data Analysis

Learn various methods of analysis including: unsupervised clustering, gene-set enrichment analyses, Bayesian integration, network visualization, and supervised machine learning applications to LINCS data and other relevant Big Data from high content molecular and phenotype profiling of human cells.

Starts : 2014-10-20
42 votes
Coursera Free Closed [?] Computer Sciences English Biology & Life Sciences Computer Science Computer Science Information Mathematics Software Engineering

This course was the first in a two-part series covering some of the algorithms underlying bioinformatics. It has now been split into three smaller courses.

Starts : 2015-03-16
No votes
Coursera Free Closed [?] Computer Sciences English Biology & Life Sciences Computer Science Computer Science Information Mathematics Software Engineering

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.

Starts : 2014-10-02
No votes
Coursera Free Closed [?] Computer Sciences Biology & Life Sciences Computer Science Computer Science Mathematics Medicine Software Engineering

This course teaches the concepts and computational methods in the exciting interdisciplinary field of bioinformatics and their applications in life sciences. The lectures are taught in both Mandarin Chinese and English with slides in English. 生物信息学是一门新兴的生命科学与计算科学的前沿交叉学科。本课程讲授生物信息学主要概念和方法,以及如何应用生物信息学手段解决生命科学问题。本课程同时提供中文普通话授课和英文授课两个版本,以及英文幻灯片。

Starts : 2016-01-25
No votes
Coursera Free Closed [?] Computer Sciences English Biology & Life Sciences Computer Science Computer Science Information Mathematics Software Engineering

Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python. It offers a gentler-paced alternative to the first course in our Bioinformatics Specialization (Finding Hidden Messages in DNA).

Starts : 2015-05-22
352 votes
Coursera Free Popular Closed [?] Computer Sciences English Engineering Mathematics

This course provides a brisk, challenging, and dynamic treatment of differential and integral calculus, with an emphasis on conceptual understanding and applications to the engineering, physical, and social sciences.

Starts : 2015-02-02
304 votes
Coursera Free Popular Computer Sciences English Artificial Intelligence Computer Science Computer Science Mathematics Statistics and Data Analysis Theory

Learn the concepts and methods of linear algebra, and how to use them to think about computational problems arising in computer science. Coursework includes building on the concepts to write small programs and run them on real data.

Starts : 2014-09-01
15 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Mathematics MIT OpenCourseWare Undergraduate

This course analyzes combinatorial problems and methods for their solution. Topics include: enumeration, generating functions, recurrence relations, construction of bijections, introduction to graph theory, network algorithms, and extremal combinatorics.

Starts : 2003-09-01
9 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Mathematics MIT OpenCourseWare Undergraduate

Combinatorial Optimization provides a thorough treatment of linear programming and combinatorial optimization. Topics include network flow, matching theory, matroid optimization, and approximation algorithms for NP-hard problems.

Starts : 2015-05-01
93 votes
Coursera Free Closed [?] Computer Sciences English Artificial Intelligence Biology & Life Sciences Computer Science Engineering Mathematics Medicine

Understanding how the brain works is one of the fundamental challenges in science today. This course will introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain. You do not need to have any prior background in neuroscience to take this course.

Starts : 2004-02-01
15 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Mathematics MIT OpenCourseWare Undergraduate

This course introduces students to iterative decoding algorithms and the codes to which they are applied, including Turbo Codes, Low-Density Parity-Check Codes, and Serially-Concatenated Codes. The course will begin with an introduction to the fundamental problems of Coding Theory and their mathematical formulations. This will be followed by a study of Belief Propagation--the probabilistic heuristic which underlies iterative decoding algorithms. Belief Propagation will then be applied to the decoding of Turbo, LDPC, and Serially-Concatenated codes. The technical portion of the course will conclude with a study of tools for explaining and predicting the behavior of iterative decoding algorithms, including EXIT charts and Density Evolution.

Starts : 2016-03-28
99 votes
Coursera Free Computer Sciences English Artificial Intelligence Computer Science Computer Science Mathematics Theory

Learn about General Game Playing (GGP) and develop GGP programs capable of competing against humans and other programs in GGP competitions .

Starts : 2016-01-04
116 votes
Coursera Free Closed [?] Computer Sciences English Artificial Intelligence Computer Science Engineering Mathematics

In this class you will look behind the scenes of image and video processing, from the basic and classical tools to the most modern and advanced algorithms.

Starts : 2004-09-01
20 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Graduate Mathematics MIT OpenCourseWare

This course introduces the basic computational methods used to understand the cell on a molecular level. It covers subjects such as the sequence alignment algorithms: dynamic programming, hashing, suffix trees, and Gibbs sampling. Furthermore, it focuses on computational approaches to: genetic and physical mapping; genome sequencing, assembly, and annotation; RNA expression and secondary structure; protein structure and folding; and molecular interactions and dynamics.

Starts : 2015-09-28
102 votes
Coursera Free Closed [?] Computer Sciences English Artificial Intelligence Computer Science Computer Science Mathematics Theory

In this course, you will learn how to formalize information and reason systematically to produce logical conclusions. We will also examine logic technology and its applications - in mathematics, science, engineering, business, law, and so forth.