Courses tagged with "Evaluation" (733)

Is my program correct?  Will it give the right output for all possible permitted inputs?  Computers are now essential in everyday life.  Incorrect programs lead to frustration in the best case and disaster in the worst. Thus, knowing how to construct correct programs is a skill that all who program computers must strive to master. 

In this computer science course, we will presents "goal oriented programming" the way Edsger Dijkstra, one of the most influential computer scientists, intended. You will learn how to derive programs hand-in-hand with their proofs of correctness. The course presents a methodology that illustrates goal-oriented programming, starting with the formalization of what is to be computed, and then growing the program hand-in-hand with its proof of correctness. The methodology demonstrates that, for a broad class of matrix operations, the development, implementation, and establishment of correctness of a program can be made systematic.

Since this technique focuses on program specifications, it often leads to clearer, correct programs in less time. The approach rapidly yields a family of algorithms from which you can then pick the algorithm that has desirable properties, such as attaining better performance on a given architecture. 

The audience of this MOOC extends beyond students and scholars interested in the domains of linear algebra algorithms and scientific computing. This course shows how to make the formal derivation of algorithms practical and will leave you pondering how our results might extend to other domains.

As a result of support from MathWorks, learners will be granted access to MATLAB for the duration of the course.

Do you have the skills to be an effective project manager in a global market?

This course focuses on leadership and management competencies, along with the ways that different organizational cultures can influence project management.

In this course, you’ll learn the personal and performance competencies required of project managers. You’ll also explore organizational culture and see how a team’s dynamics affect a project. Finally, you’ll delve into team composition and communications, as well as the links between organizational change management and readiness.

Ready to start your programming journey? Being a software engineer is much more than simply writing code—it requires a strong conceptual understanding of computer science. In this course, which was developed through a combination of academic and industry perspectives, learn not only how to code in Java but also how to break down problems and implement their solutions using some of the most fundamental computer science tools.

Get plenty of hands-on Java coding experience with methods, logic, loops, variables, parameters, returns, and recursion. And write your code using industry-standard tools and practices to help you build strong habits as you grow your development skill set.

Whether you are preparing for advanced university computer science courses, an entry-level software engineering position, or the Advanced Placement Computer Science A exam, get the tools you need to succeed in this practical, self-paced Java course.

Interested in learning a computer programming language but unsure of how and where to begin? This course, Learn to Program Using Python, is a great place to start.

Python is an easy and fun language to learn, and it is now one of the most popular programming languages, suitable for almost any task from developing graphical user interfaces to building web applications.

This course is an introduction to the Python programming language. This course is open to all learners who wish to gain an understanding of the basic components of computer programming. You will learn basic computer programming concepts and terminologies such as variables, constants, operators, expressions, conditional statements, loops, and functions. This Python course includes hands-on exercises to help you understand the components of Python programming while incrementally developing more significant programs. The exercises in this course will be based on small assignments which will relate to real-world problems.

No previous programming knowledge needed.

Not all programs are created equal.  In this course, we'll focus on writing quality code that runs correctly and efficiently.  We'll design, code and validate our programs and learn how to compare programs that are addressing the same task.

Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.

This introductory computer science course in machine learning will cover basic theory, algorithms, and applications. Machine learning is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. Machine learning has become one of the hottest fields of study today and the demand for jobs is only expected to increase. Gaining skills in this field will get you one step closer to becoming a data scientist or quantitative analyst.

This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion:

• What is learning?
• Can a machine learn?
• How to do it?
• How to do it well?
• Take-home lessons.

Dieser Kurs bietet einen leicht zugänglichen Einstieg in die faszinierende Welt der Informatik. Dabei werden insbesondere die objekt-orientierte Programmierung und einfache Algorithmen behandelt. Sie lernen unter anderem, wie man kleine Programme in der populären Programmiersprache Java schreibt.

Dies ist aber kein reiner Programmierkurs, sondern soll vor allem die wesentlichen Grundlagen bereitstellen, um auch größere Programme verstehen und selbst entwickeln zu können. Bei den vielen interaktiven Übungen mit praktischen Beispielen und direkten Rückmeldungen kommt aber auch der Spaß nicht zu kurz!

Der Kurs orientiert sich am bayerischen Schulfach Informatik, das von Prof. Hubwieser maßgeblich mitkonzipiert wurde.

Linear Algebra: Foundations to Frontiers (LAFF) is packed full of challenging, rewarding material that is essential for mathematicians, engineers, scientists, and anyone working with large datasets. Students appreciate our unique approach to teaching linear algebra because:

• It’s visual.
• It connects hand calculations, mathematical abstractions, and computer programming.
• It illustrates the development of mathematical theory. 
• It’s applicable.

In this course, you will learn all the standard topics that are taught in typical undergraduate linear algebra courses all over the world, but using our unique method, you'll also get more! LAFF was developed following the syllabus of an introductory linear algebra course at The University of Texas at Austin taught by Professor Robert van de Geijn, an expert on high performance linear algebra libraries. Through short videos, exercises, visualizations, and programming assignments, you will study Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear Least-Squares, and Eigenvalues and Eigenvectors. In addition, you will get a glimpse of cutting edge research on the development of linear algebra libraries, which are used throughout computational science.

MATLAB licenses will be made available to the participants free of charge for the duration of the course.

We invite you to LAFF with us!

Foundations to Frontiers (LAFF) is packed full of challenging, rewarding material that is essential for mathematicians, engineers, scientists, and anyone working with large datasets. Students appreciate our unique approach to teaching linear algebra because:

• It’s visual.
• It connects hand calculations, mathematical abstractions, and computer programming.
• It illustrates the development of mathematical theory.
• It’s applicable.

In this course, you will learn all the standard topics that are taught in typical undergraduate linear algebra courses all over the world, but using our unique method, you'll also get more! LAFF was developed following the syllabus of an introductory linear algebra course at The University of Texas at Austin taught by Professor Robert van de Geijn, an expert on high performance linear algebra libraries. Through short videos, exercises, visualizations, and programming assignments, you will study Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear Least-Squares, and Eigenvalues and Eigenvectors. In addition, you will get a glimpse of cutting edge research on the development of linear algebra libraries, which are used throughout computational science.

MATLAB licenses will be made available to the participants free of charge for the duration of the course.

This summer version of the course will be released at an accelerated pace. Each of the three releases will consist of four ”Weeks” plus an exam . There will be suggested due dates, but only the end of the course is a true deadline.

We invite you to LAFF with us!

FAQs

What is the estimated effort for the course? 

About 8 hrs/week.

How much does it cost to take the course?

You can choose! Auditing the course is free.  If you want to challenge yourself by earning a Verified Certificate of Achievement, the contributions start at $50.

Will the text for the videos be available?

Yes. All of our videos will have transcripts synced to the videos.

Are notes available for download?

PDF versions of our notes will be available for free download from the edX platform during the course.  Compiled notes are currently available at www.ulaff.net.

Do I need to watch the videos live?

No. You watch the videos at your leisure.

Can I contact the Instructor or Teaching Assistants?

Yes, but not directly. The discussion forums are the appropriate venue for questions about the course. The instructors will monitor the discussion forums and try to respond to the most important questions; in many cases response from other students and peers will be adequate and faster.

Is this course related to a campus course of The University of Texas at Austin?

Yes. This course corresponds to the Division of Statistics and Scientific Computing titled “SDS329C: Practical Linear Algebra”, one option for satisfying the linear algebra requirement for the undergraduate degree in computer science.

Is there a certificate available for completion of this course?

Online learners who successfully complete LAFF can obtain an edX certificate. This certificate indicates that you have successfully completed the course, but does not include a grade.

Must I work every problem correctly to receive the certificate?

No, you are neither required nor expected to complete every problem.

What textbook do I need for the course?

There is no textbook. PDF versions of our notes will be available for free download from the edX platform during the course. Compiled notes are currently available at www.ulaff.net.

What are the principles by which assignment due dates are established?

There is a window of 19 days between the material release and the due date for the homework of that week. While we encourage you to complete a week’s work before the launch of the next week, we realize that life sometimes gets in the way so we have established a flexible cushion. Please don’t procrastinate. The course closes 25 May 2015. This is to give you nineteen days from the release of the final to complete the course.

Are there any special system requirements?

You may need at least 768MB of RAM memory and 2-4GB of free hard drive space. You should be able to successfully access the course using Chrome and Firefox.

The course is an introduction to linear and discrete optimization - an important part of computational mathematics with a wide range of applications in many areas of everyday life.

Linux professionals are in high demand with >50K open jobs today. 97% of hiring managers want Linux talent because Linux is the #1 Internet, Cloud, mobile and CE OS.

Show employers you have the skills and the knowledge to be a top sysadmin with the Linux Foundation Certified System Administrator (LFCS).

This Linux course is for those just starting their career in IT as well as professionals with experience on other operating systems who want to add Linux to their portfolio.

This sysadmin course will help you develop the fundamental skills that employers look for in entry level Linux System Administrators by focusing heavily on hands-on practice via nearly 100 lab exercises.

Entry level certification is the best way for candidates who are new to Linux to show their capability. After finishing the course on edX, learners will be able to register for LFCS Exam for free. You will then have 12 months to complete the exam from registration date. You can also take advantage of a free exam retake, if needed.

Topics covered in the course include:

• System startup and kernel basics
• Working with disks and filesystems
• Process management and system monitoring
• Package management systems
• User and Group permissions
• Security and authentication
• Network addresses and devices
• Troubleshooting and system rescue

Understanding how a computer "thinks" is one of the first steps to becoming an excellent computer programmer. A foundation in logic is crucial in developing this understanding. Mastering logic is more than learning a set of rules. It involves learning how to break problems into smaller chunks, figuring out how repeatable processes can save time and improve quality, and understanding how to organize problems into the right size.

In this course, you'll learn how to do all those things and use computers to make them easier. After all, logical tasks are what computers are best at doing!

This is not a programming course, but it will teach you how to approach critical thinking as both a lifestyle and an aide to better programming and testing.

This is an introduction to formal logic and how it is applied in computer science, electronic engineering, linguistics and philosophy. You will learn propositional logic—its language, interpretations and proofs, and apply it to solve problems in a wide range of disciplines.

This is an introduction to predicate logic and how it is applied in computer science, electronic engineering, linguistics, mathematics and philosophy. Building on your knowledge of propositional logic, you will learn predicate logic—its language, interpretations and proofs, and apply it to solve problems in a wide range of disciplines.

If you invest in financial markets, you may want to predict the price of a stock in six months from now on the basis of company performance measures and other economic factors. As a college student, you may be interested in knowing the dependence of the mean starting salary of a college graduate, based on your GPA. These are just some examples that highlight how statistics are used in our modern society. To figure out the desired information for each example, you need data to analyze. The purpose of this course is to introduce you to the subject of statistics as a science of data. There is data abound in this information age; how to extract useful knowledge and gain a sound understanding in complex data sets has been more of a challenge. In this course, we will focus on the fundamentals of statistics, which may be broadly described as the techniques to collect, clarify, summarize, organize, analyze, and interpret numerical information. This course will begin with a brief overview of the discipline of stat…

Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real world situations ranging from identifying trending news topics, to building recommendation engines, ranking sports teams and plotting the path of movie zombies.

Major perspectives covered include:

• probabilistic versus non-probabilistic modeling
• supervised versus unsupervised learning

Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling and model selection.

Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

In the first half of the course we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.

In the second half of the course we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. This area is also concerned with issues both theoretical and practical.

In this course, we will present algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:

• statistical supervised and unsupervised learning methods
• randomized search algorithms
• Bayesian learning methods
• reinforcement learning

The course also covers theoretical concepts such as inductive bias, the PAC and Mistake‐bound learning frameworks, minimum description length principle, and Ockham's Razor. In order to ground these methods the course includes some programming and involvement in a number of projects.
 
By the end of this course, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning. 

This is a three-credit course.