Online courses directory (224)

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Starts : 2008-09-01
10 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Graduate Health Sciences and Technology MIT OpenCourseWare

This team-taught multidisciplinary course provides information relevant to the conduct and interpretation of human brain mapping studies. It begins with in-depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include: fMRI experimental design including block design, event related and exploratory data analysis methods, and building and applying statistical models for fMRI data; and human subject issues including informed consent, institutional review board requirements and safety in the high field environment.

Additional Faculty

Div Bolar

Dr. Bradford Dickerson

Dr. John Gabrieli

Dr. Doug Greve

Dr. Karl Helmer

Dr. Dara Manoach

Dr. Jason Mitchell

Dr. Christopher Moore

Dr. Vitaly Napadow

Dr. Jon Polimeni

Dr. Sonia Pujol

Dr. Bruce Rosen

Dr. Mert Sabuncu

Dr. David Salat

Dr. Robert Savoy

Dr. David Somers

Dr. A. Gregory Sorensen

Dr. Christina Triantafyllou

Dr. Wim Vanduffel

Dr. Mark Vangel

Dr. Lawrence Wald

Dr. Susan Whitfield-Gabrieli

Dr. Anastasia Yendiki



Other Versions

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Starts : 2016-11-18
No votes
Kadenze Free Creative Computing Visual Arts

This course proposes an introduction and overview of the history and practice of generative arts and computational creativity with an emphasis on the formal paradigms and algorithms used for generation.

On the technical side, we will study core techniques from mathematics, artificial intelligence, and artificial life that are used by artists, designers and musicians across the creative industry. We will start with processes involving chance operations, chaos theory and fractals and move on to see how stochastic processes, and rule-based approaches can be used to explore creative spaces. We will study agents and multi-agent systems and delve into cellular automata, and virtual ecosystems to explore their potential to create novel and valuable artifacts and aesthetic experiences.

The presentation is illustrated by numerous examples from past and current productions across creative practices such as visual art, new media, music, poetry, literature, performing arts, design, architecture, games, robot-art, bio-art and net-art. Students get to practice these algorithms first hand and develop new generative pieces through assignments and projects in MAX. Finally, the course addresses relevant philosophical, and societal debates associated with the automation of creative tasks.

Music for this course was composed with the StyleMachineLite Max for Live engine of Metacreative Inc.
Artistic direction: Philippe Pasquier, Programmation: Arne Eigenfeldt, Sound Production: Philippe
Bertrand

Starts : 2016-03-09
No votes
Coursera Free Closed [?] English Biology & Life Sciences Computer Science Computer Science Mathematics Software Engineering Theory

Biologists still cannot read the nucleotides of an entire genome as you would read a book from beginning to end. However, they can read short pieces of DNA. In this course, we will see how graph theory can be used to assemble genomes from these short pieces. We will further learn about brute force algorithms and apply them to sequencing mini-proteins called antibiotics. Finally, you will learn how to apply popular bioinformatics software tools to sequence the genome of a deadly Staphylococcus bacterium.

Starts : 2012-09-01
No votes
MIT OpenCourseWare (OCW) Free Electrical Engineering and Computer Science Graduate MIT OpenCourseWare

This course focuses on the algorithms for analyzing and designing geometric foldings. Topics include reconfiguration of foldable structures, linkages made from one-dimensional rods connected by hinges, folding two-dimensional paper (origami), and unfolding and folding three-dimensional polyhedra. Applications to architecture, robotics, manufacturing, and biology are also covered in this course.

Acknowledgments

Thanks to videographers Martin Demaine and Jayson Lynch.

Starts : 2008-02-01
10 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Electrical Engineering and Computer Science MIT OpenCourseWare Undergraduate

This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of "computer science beyond computers": that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. Beginning in antiquity—with Euclid's algorithm and other ancient examples of computational thinking—the course will progress rapidly through propositional logic, Turing machines and computability, finite automata, Gödel's theorems, efficient algorithms and reducibility, NP-completeness, the P versus NP problem, decision trees and other concrete computational models, the power of randomness, cryptography and one-way functions, computational theories of learning, interactive proofs, and quantum computing and the physical limits of computation. Class participation is essential, as the class will include discussion and debate about the implications of many of these ideas.

Starts : 2008-02-01
No votes
MIT OpenCourseWare (OCW) Free Closed [?] Electrical Engineering and Computer Science MIT OpenCourseWare Undergraduate

This course provides a challenging introduction to some of the central ideas of theoretical computer science. It attempts to present a vision of "computer science beyond computers": that is, CS as a set of mathematical tools for understanding complex systems such as universes and minds. Beginning in antiquity—with Euclid's algorithm and other ancient examples of computational thinking—the course will progress rapidly through propositional logic, Turing machines and computability, finite automata, Gödel's theorems, efficient algorithms and reducibility, NP-completeness, the P versus NP problem, decision trees and other concrete computational models, the power of randomness, cryptography and one-way functions, computational theories of learning, interactive proofs, and quantum computing and the physical limits of computation. Class participation is essential, as the class will include discussion and debate about the implications of many of these ideas.

No votes
Udacity Free Closed [?] Georgia Tech Masters in CS

The goal of this course is to give you solid foundations for developing, analyzing, and implementing parallel and locality-efficient algorithms. This course focuses on theoretical underpinnings. To give a practical feeling for how algorithms map to and behave on real systems, we will supplement algorithmic theory with hands-on exercises on modern HPC systems, such as Cilk Plus or OpenMP on shared memory nodes, CUDA for graphics co-processors (GPUs), and MPI and PGAS models for distributed memory systems. This course is a graduate-level introduction to scalable parallel algorithms. “Scale” really refers to two things: efficient as the problem size grows, and efficient as the system size (measured in numbers of cores or compute nodes) grows. To really scale your algorithm in both of these senses, you need to be smart about reducing asymptotic complexity the way you’ve done for sequential algorithms since CS 101; but you also need to think about reducing communication and data movement. This course is about the basic algorithmic techniques you’ll need to do so. The techniques you’ll encounter covers the main algorithm design and analysis ideas for three major classes of machines: for multicore and many core shared memory machines, via the work-span model; for distributed memory machines like clusters and supercomputers, via network models; and for sequential or parallel machines with deep memory hierarchies (e.g., caches). You will see these techniques applied to fundamental problems, like sorting, search on trees and graphs, and linear algebra, among others. The practical aspect of this course is implementing the algorithms and techniques you’ll learn to run on real parallel and distributed systems, so you can check whether what appears to work well in theory also translates into practice. (Programming models you’ll use include Cilk Plus, OpenMP, and MPI, and possibly others.)

Starts : 2017-07-12
No votes
edX Free Closed [?] English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction and multi-dimensional scaling and its connection to principle component analysis. We will learn about the batch effect: the most challenging data analytical problem in genomics today and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data.

Finally, we give a brief introduction to machine learning and apply it to high-throughput data. We describe the general idea behind clustering analysis and descript K-means and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as k-nearest neighbors along with the concepts of training sets, test sets, error rates and cross-validation.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics


This class was supported in part by NIH grant R25GM114818.

HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

1 votes
Open.Michigan Initiative, University of Michigan Free Computer Sciences Computing science Cyberinfrastructure Cyberscience Information technology Scientific Computing

In the last half of the 20th century, the role of computation in the sciences grew rapidly, driven by advances in silicon-based processors, fiber-optic networks, a host of numerical algorithms, and sets of standard protocols for processing and exchanging data. Much of this digital technology now permeates everyday life. Building on these and emerging technologies, the 21st century is poised to unleash a new, data-intensive paradigm of scientific discovery that will dramatically enhance the scope and scale of data capture, curation, and analysis. In this new (4th) paradigm, cures for cancer might be found by the collective investigations of agents computing "in the cloud.

Starts : 2017-03-06
No votes
edX Free Closed [?] English Computer Science Data Analysis & Statistics EdX ITMOx Math

Want to be the programmer hot tech companies are looking for?

Take your programming skills to the next level and prove your excellence by learning how to succeed in programming competitions.

Besides improving your knowledge of algorithms and programming languages, you’ll gain unique experience in problem solving, thinking outside the box and meeting tough deadlines – all essential for boosting your value as a programmer and securing a coveted job in Silicon Valley (should you want one).

This computer science course is an introduction to competitive programming developed by ITMO University, the leading expert in IT and the only 6-time world champion of the Association for Computing Machinery - International Collegiate Programming Contest (ACM ICPC), the world's most prestigious programming contest.

You will learn all you need to know about the variety of programming competitions that exist, as well as basic algorithms and data structures necessary to succeed in the most popular of them.

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 : 2017-02-21
No votes
edX Free Closed [?] English Computer Science EdX IITBombayX

In this Computer Science course, you will learn about implementation of all major abstract data structures using object-oriented programming paradigm of C++.

This course builds on the basic concepts developed in ‘Foundations of Data Structures’ course.

Topics covered: 

  • Review of OO programming, STL of C++
  • Stacks
  • Queues
  • Lists
  • Trees
  • Graphs

This course is part of the Fundamentals of Computer Science XSeries Program

Starts : 2009-09-01
9 votes
MIT OpenCourseWare (OCW) Free Business Graduate MIT OpenCourseWare Sloan School of Management

The course is a comprehensive introduction to the theory, algorithms and applications of integer optimization and is organized in four parts: formulations and relaxations, algebra and geometry of integer optimization, algorithms for integer optimization, and extensions of integer optimization.

100 votes
Udacity Free Closed [?] Computer Sciences Software Engineering

Ever played the Kevin Bacon game? This class will show you how it works by giving you an introduction to the design and analysis of algorithms, enabling you to discover how individuals are connected.

No votes
Udacity Free Closed [?] Data Science

Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions. This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms. This course is also a part of our Data Analyst Nanodegree.

98 votes
Udacity Free Closed [?] Computer Sciences Software Engineering

Learn the fundamentals of parallel computing with the GPU and the CUDA programming environment! In this class, you'll learn about parallel programming by coding a series of image processing algorithms, such as you might find in Photoshop or Instagram. You'll be able to program and run your assignments on high-end GPUs, even if you don't own one yourself. **Why It’s Important to Think Parallel** [Third Pillar of Science][1] Learn how scientific discovery can be accelerated by combining theory and experimentation with computing to fight cancer, prevent heart attacks, and spur new advances in robotic surgery. [1]: http://www.youtube.com/watch?v=3DbAB2ChDBw

Starts : 2011-09-01
20 votes
MIT OpenCourseWare (OCW) Free Computer Sciences Electrical Engineering and Computer Science MIT OpenCourseWare Undergraduate

This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performance measures and analysis techniques for these problems.

Starts : 2017-08-21
No votes
edX Free Closed [?] English Data Analysis & Statistics EdX Engineering GTx

Please note that the verified certificate option for this course is limited to 300 learners. The verified certificate option will close when this limit is reached.

Analytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.  

In this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.

You’ll learn about analytics modeling and how to choose the right approach from among the wide range of options in your toolbox.

You will learn how to use statistical models and machine learning as well as models for:

  • classification;
  • clustering;
  • change detection;
  • data smoothing;
  • validation;
  • prediction;
  • optimization;
  • experimentation;
  • decision making.

Starts : 2017-09-07
No votes
edX Free Closed [?] English Biology & Life Sciences Data Analysis & Statistics EdX HarvardX Science

We begin with an introduction to the biology, explaining what we measure and why. Then we focus on the two main measurement technologies: next generation sequencing and microarrays. We then move on to describing how raw data and experimental information are imported into R and how we use Bioconductor classes to organize these data, whether generated locally, or harvested from public repositories or institutional archives. Genomic features are generally identified using intervals in genomic coordinates, and highly efficient algorithms for computing with genomic intervals will be examined in detail. Statistical methods for testing gene-centric or pathway-centric hypotheses with genome-scale data are found in packages such as limma, some of these techniques will be illustrated in lectures and labs.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

These courses make up 2 XSeries and are self-paced:

PH525.1x: Statistics and R for the Life Sciences

PH525.2x: Introduction to Linear Models and Matrix Algebra

PH525.3x: Statistical Inference and Modeling for High-throughput Experiments

PH525.4x: High-Dimensional Data Analysis

PH525.5x: Introduction to Bioconductor: annotation and analysis of genomes and genomic assays 

PH525.6x: High-performance computing for reproducible genomics

PH525.7x: Case studies in functional genomics


This class was supported in part by NIH grant R25GM114818.

HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

HarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our research statement to learn more.

Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact harvardx@harvard.edu and/or report your experience through the edX contact form.

Starts : 2017-07-14
No votes
edX Free Closed [?] English Computer Science EdX Microsoft

There are many programming languages in use today. Choosing which language to program in can be based on many factors such as learning curve, job specific requirements, platform specifics, or a plethora of other criteria. In this course, you will be introduced to the C# language and the world of .NET programming.  

The C# programming language was created from the ground up to be an object-oriented programming language that offers ease of use, familiarity to C/C++ and Java developers, along with enhanced memory and resource management. C# is prevalent on the Microsoft platform but is also being used to develop software that runs on Linux, Android, and iOS devices as well. 

Learning C# can position you for future programming opportunities, provide a solid foundation in object-oriented programming knowledge, and pave the way for learning other programming languages. This course aims to teach you about the core aspects of the C# language.

This course is the first part of a three-part series designed to teach core C# language fundamentals. In the second course of the series, you will learn object-oriented programming concepts and the third course offers instruction on data structures and algorithms with C#. Once you complete the series, you will have a very good foundation of C# knowledge to expand on and learn more as you progress in your programming career or hobby.

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