Learn a new way to approach problem solving by stating the problem and letting powerful constraint solving software do the rest. This class teaches you the art of encoding complex discrete optimization problems in the MiniZinc modeling language and then shows you how to effortlessly solve them by leveraging state-of-the-art open-source constraint solving software.
The world is full of hard discrete optimization problems. You have experienced them already if you have ever solved a sudoku puzzle or organized the seating at a wedding banquet. These problems underpin much of our daily lives and are part of determining daily delivery routes for packages, making school timetables, and delivering power to our homes. Despite their fundamental importance, all of these problems are a nightmare to solve using traditional undergraduate computer science methods.
In this course you will learn an entirely new way to think about solving these challenging discrete optimization problems by stating the problem in the high level modeling language, MiniZinc, and letting constraint solving software do the rest. This will allow you to unlock the power of industrial solving technologies, which have been perfected over decades by hundreds of PhD researchers. With access to this advanced technology, problems that you could not conceive solving before will suddenly become easy and bring you one step closer to "the holy grail of programming: the user states the problem, the computer solves it." (Eugene Freuder '96)
Learn the principles of machine learning and the importance of algorithms. Machine Learning is a growing field that is used when searching the web, placing ads, credit scoring, stock trading and for many other applications. This data science course is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. We will also examine why algorithms play an essential role in Big Data analysis.
De nombreux secteurs industriels (transports, bâtiment, loisirs, ...) s'intéressent aux matériaux composites dans un souci d'allègement des structures. Une utilisation efficace de ces matériaux requière la maîtrise de l'ensemble du processus de conception, du concept initial au produit final. La modélisation de la réponse mécanique d'une pièce composite est une étape essentielle pour assurer son adéquation au cahier de charges. Ce cours vous permettra d'acquérir les principaux outils nécessaires à la description du comportement des composites stratifiés aux différentes échelles d'observation, de celle de la fibre à celle de la pièce. Ce MOOC est un module indépendant de l'ensemble 'Pratiques en Conception des Structures Composites' : fabrication et caractérisation, modélisation.
Welcome to Taylor’s School of Design & Architecture (SABD) Massive Online Open Course (MOOC). In Building Information Modeling 202, your instructor Bruce Lee will guide you to explore some of the interesting features in building information modeling (BIM) and with his guidance you will model a multi-storey building with BIM software. Students are also to explore more about nD modeling with different tool and software.
This course introduces simple and multiple linear regression models. These models allow you to assess the relationship between variables in a data set and a continuous response variable. Is there a relationship between the physical attractiveness of a professor and their student evaluation scores? Can we predict the test score for a child based on certain characteristics of his or her mother? In this course, you will learn the fundamental theory behind linear regression and, through data examples, learn to fit, examine, and utilize regression models to examine relationships between multiple variables, using the free statistical software R and RStudio.
Once you’ve identified a big data issue to analyze, how do you collect, store and organize your data using Big Data solutions? In this course, you will experience various data genres and management tools appropriate for each. You will be able to describe the reasons behind the evolving plethora of new big data platforms from the perspective of big data management systems and analytical tools.
Welcome to Quantitative Formal Modeling and Worst-Case Performance Analysis. In this course, you will learn about modeling and solving performance problems in a fashion popular in theoretical computer science, and generally train your abstract thinking skills.