Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."

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Oct 27th 2014

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Modéliser un problème, concevoir un algorithme de résolution et en proposer une implémentation correcte. Du problème à sa solution, ce cours combine approches pragmatique, pratique et théorique de l'informatique.

Ce cours vous fera découvrir différents aspects de la conception d'un programme. Au travers de nombreuses études de cas, nous mettrons en exergue les structures de données et les algorithmes permettant d'apporter des solutions. Comme souvent en informatique, il n'existe pas de solution unique et nous serons amenés à découvrir différentes classes d'algorithme et à les comparer.

Nous introduirons à cet effet la notion de complexité d'un programme, c'est à dire à la fois une estimation du temps d'exécution de votre programme et de l'espace requis par celui-ci. Il est tentant de croire que le "meilleur" programme est celui qui minimise le temps d'exécution mais très souvent cette complexité est contrainte par la mémoire dont vous disposez. Ainsi, vous n'utiliserez peut-être pas le même algorithme selon que votre programme s'exécute sur un ordinateur ou un téléphone !

Description: Ce cours présentera les structures de données les plus classiques comme les tableaux, listes, piles, files, et arbres pour aller vers les graphes.

En parallèle, on découvrira les grands concepts de l'algorithmique à travers des études cas. Nous passerons ainsi en revue les tris, parcours, les arbres de recherche quaternaires, les algorithmes gloutons ainsi que les bases de la programmation dynamique.

Bonne connaissance d'un langage de programmation et des bases de java, les exemples et exercices étant donnés dans ce langage.

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Sep 17th 2017

Introductory Machine Learning course covering theory, algorithms and applications. Our focus is on real understanding, not just "knowing."

Sep 11th 2017

In this course you will learn how to apply the functional programming style in the design of larger applications. You'll get to know important new functional programming concepts, from lazy evaluation to structuring your libraries using monads. We'll work on larger and more involved examples, from state space exploration to random testing to discrete circuit simulators. You’ll also learn some best practices on how to write good Scala code in the real world.

Sep 11th 2017

In this course you will learn a whole lot of modern physics (classical and quantum) from basic computer programs that you will download, generalize, or write from scratch, discuss, and then hand in. Join in if you are curious (but not necessarily knowledgeable) about algorithms, and about the deep insights into science that you can obtain by the algorithmic approach.

Sep 11th 2017

World and internet is full of textual information. We search for information using textual queries, we read websites, books, e-mails. All those are strings from the point of view of computer science. To make sense of all that information and make search efficient, search engines use many string algorithms. Moreover, the emerging field of personalized medicine uses many search algorithms to find disease-causing mutations in the human genome.

Sep 11th 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.

Sep 11th 2017

With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm.

Sep 11th 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.

Sep 11th 2017

En este curso aprenderemos los fundamentos del diseño de los circuitos digitales actuales, siguiendo una orientación eminentemente práctica. A diferencia de otros cursos más "clásicos" de Circuitos Digitales, nuestro interés se centrará más en el Sistema que en la Electrónica que lo sustenta. Este enfoque nos permitirá sentar las bases del diseño de Sistemas Digitales complejos.

Sep 11th 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.

Sep 11th 2017

Data analysis has replaced data acquisition as the bottleneck to evidence-based decision making --- we are drowning in it. Extracting knowledge from large, heterogeneous, and noisy datasets requires not only powerful computing resources, but the programming abstractions to use them effectively. The abstractions that emerged in the last decade blend ideas from parallel databases, distributed systems, and programming languages to create a new class of scalable data analytics platforms that form the foundation for data science at realistic scales.

Sep 11th 2017

The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

Sep 11th 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.

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