Algorithms

 

 


 

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E.g., 2017-04-25
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May 15th 2017

Ce cours explique comment l’informatique contribue à l’analyse de l’information génétique. Il introduit conjointement les notions de génomique et d’algorithmique impliquées. Dans ce cours, nous verrons comment l’informatique permet d’interpréter le texte des génomes. Doté d’algorithmes adaptés, mis en œuvre sous forme de programmes efficaces, l’ordinateur produit des prédictions quant à la localisation des milliers de gènes d’un organisme vivant et les fonctions que remplissent les protéines qu’ils codent.

Average: 3 (1 vote)
May 15th 2017

This course explains how computer science supports the interpretation of the text of genomes. It introduces genomics and algorithmics in a joint approach.

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Self Paced

Learn key computer science topics to better teach your students and prepare for the Praxis & TExES Computer Science certification test. Thinking about teaching Computer Science, or incorporating CS into a subject you already teach? CS skills are fundamental to student success and you can teach it! This course is intended for prospective K-12 Computer Science teachers to review key topics in Technology Applications, Programming Languages and CS special topics.

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May 1st 2017

本课程的目的有二:其一,帮助同学们了解计算机的基本运行原理,了解程序运行的基本原理,了解计算机的发展状态及趋势。其二,引导同学们逐步进入"计算机程序设计语言"的学习。我们希望本课程为同学们解答如下一些问题:计算机为什么能够进行计算?计算机程序是怎样运行的?计算机未来可能的发展趋势有哪些?程序是如何编写出来的?如何学习程序设计语言?程序设计语言的基本成分有哪些? 完成本课程,表明同学们已经了解了计算机运行的基本原理,了解了计算机程序的基本特性。

Average: 1 (1 vote)
May 1st 2017

If you have ever used a navigation service to find optimal route and estimate time to destination, you've used algorithms on graphs. Graphs arise in various real-world situations as there are road networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs.

Average: 7.8 (12 votes)
May 1st 2017

In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user.

Average: 6.5 (2 votes)
May 1st 2017

这门课程将帮助学生学习如何运用高级的数据结构和相关算法解决复杂的应用问题。

Average: 4.7 (3 votes)
May 1st 2017

这门课程将帮助学生学习如何运用基础的数据结构和相关算法解决实际应用问题。

Average: 4.5 (2 votes)
May 1st 2017

Analytic Combinatorics teaches a calculus that enables precise quantitative predictions of large combinatorial structures. This course introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotics from the GF equations.

Average: 4.3 (12 votes)
May 1st 2017

You've learned the basic algorithms now and are ready to step into the area of more complex problems and algorithms to solve them. Advanced algorithms build upon basic ones and use new ideas. We will start with networks flows which are used in more obvious applications such as optimal matchings, finding disjoint paths and flight scheduling as well as more surprising ones like image segmentation in computer vision or finding dense clusters in the advertiser-search query graphs at search engines. We then proceed to linear programming with applications in optimizing budget allocation, portfolio optimization, finding the cheapest diet satisfying all requirements, call routing in telecommunications and many others. Next we discuss inherently hard problems for which no exact good solutions are known (and not likely to be found) and how to solve them approximately in a reasonable time. We finish with some applications to Big Data and Machine Learning which are heavy on algorithms right now.

Average: 6.6 (17 votes)
May 1st 2017

Use computer programming as a creative discipline to generate sounds, images, animations and more, with this free online course. Learning to program is no longer just for computer specialists and software developers. People from many different backgrounds now want to understand the basics of programming, because it’s both fun and an increasingly valuable skill.

Average: 10 (1 vote)
May 1st 2017

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).

Average: 5 (2 votes)
May 1st 2017

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.

Average: 6.5 (11 votes)
May 1st 2017

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

Average: 7.6 (31 votes)
May 1st 2017

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.

Average: 6.5 (2 votes)
May 1st 2017

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.

Average: 5.9 (18 votes)
May 1st 2017

Case Study - Predicting Housing Prices
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.

Average: 7.5 (4 votes)
May 1st 2017

How do Java programs deal with vast quantities of data? Many of the data structures and algorithms that work with introductory toy examples break when applications process real, large data sets. Efficiency is critical, but how do we achieve it, and how do we even measure it? In this course, you will use and analyze data structures that are used in industry-level applications, such as linked lists, trees, and hashtables.

Average: 6.3 (4 votes)
May 1st 2017

Experienced Computer Scientists analyze and solve computational problems at a level of abstraction that is beyond that of any particular programming language. This two-part class is designed to train students in the mathematical concepts and process of "Algorithmic Thinking", allowing them to build simpler, more efficient solutions to computational problems.

Average: 6.3 (9 votes)
May 1st 2017

The course covers basic algorithmic techniques and ideas for computational problems arising frequently in practical applications: sorting and searching, divide and conquer, greedy algorithms, dynamic programming. We will learn a lot of theory: how to sort data and how it helps for searching; how to break a large problem into pieces and solve them recursively; when it makes sense to proceed greedily; how dynamic programming is used in genomic studies. You will practice solving computational problems, designing new algorithms, and implementing solutions efficiently (so that they run in less than a second).

Average: 7.6 (14 votes)

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