Online courses directory (19947)

Computing for the rest of us: finally get started with Java. Computer programming could save your career!

Learn computer science for beginners explained in a kid friendly language.

Learn how to design secure systems and write secure code.

Gain a working knowledge of the computer skills you'll need to succeed in today's job market.

6.823 is a course in the department's "Computer Systems and Architecture" concentration. 6.823 is a study of the evolution of computer architecture and the factors influencing the design of hardware and software elements of computer systems. Topics may include: instruction set design; processor micro-architecture and pipelining; cache and virtual memory organizations; protection and sharing; I/O and interrupts; in-order and out-of-order superscalar architectures; VLIW machines; vector supercomputers; multithreaded architectures; symmetric multiprocessors; and parallel computers.

In this computer science course, you will learn advanced concepts underpinning the design of today’s multicore-based computers. Additionally, you will learn how design decisions affect energy efficiency and performance.

Overall, topics include fundamentals on exploiting parallelism among instructions such as out-of-order execution, branch prediction, exception handling and advanced concepts of memory systems including prefetching and cache coherency. These concepts are fundamental for future computer systems to maximize compute efficiency.

You will also engage with a community of learners with similar interests to share knowledge.

The course is derived from Chalmers’s advanced graduate course in computer architecture. Prospective students should have a foundation in basic computer design, as offered by, for example, in “Computer System Design - Improving Energy Efficiency and Performance.“

This course covers topics on the engineering of computer software and hardware systems: techniques for controlling complexity; strong modularity using client-server design, virtual memory, and threads; networks; atomicity and coordination of parallel activities; recovery and reliability; privacy, security, and encryption; and impact of computer systems on society. Case studies of working systems and readings from the current literature provide comparisons and contrasts. Two design projects are required, and students engage in extensive written communication exercises.

In order to be competitive in the computer science field, it’s imperative to understand the basic building blocks of a modern computer and how they directly impact the speed and efficiency of a program. Whether you work with embedded systems, mobile computer-based systems, or cloud systems, performance and energy efficiency are key drivers of usability and competitiveness of computerized products.

In this course, you will learn how to design modern multicore-based computers, and how the design choices you make affect performance and energy consumption. You will explore design principles governing modern microprocessors, such as pipelining and cache memories, as well as methods for determining the impact of your design on execution time and energy efficiency.

These skills can make a difference for practicing engineers for the purpose of building highly competitive products. Take, for instance, a smartphone with limited battery capacity. By adding value to end users through new or improved functionality, this can lead to significantly shorter operation time between battery charges, thus utilizing the already limited resources of a smartphone far more efficiently.

With this skillset, you can become an expert in computer system performance and energy efficiency - knowledge that is in high demand when designing computerized embedded products. With trends towards IoT (Internet of Things), autonomous systems and mobile computers, such a skillset will be critical in a career in systems engineering.

This course is derived from a Chalmers senior undergraduate course in computer system design. It assumes basic knowledge in computer organization.

6.858 Computer Systems Security is a class about the design and implementation of secure computer systems. Lectures cover threat models, attacks that compromise security, and techniques for achieving security, based on recent research papers. Topics include operating system (OS) security, capabilities, information flow control, language security, network protocols, hardware security, and security in web applications.

This course delivers a systematic overview of computer vision, emphasizing two key issues in modeling vision: space and meaning. We will study the fundamental theories and important algorithms of computer vision together, starting from the analysis of 2D images, and culminating in the holistic understanding of a 3D scene.

In this course, we will study the concepts and algorithms behind some of the remarkable successes of computer vision - capabilities such as face detection, handwritten digit recognition, reconstructing three-dimensional models of cities and more.

This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.

This course introduces abstraction as an important mechanism for problem decomposition and solution formulation in the biomedical domain, and examines computer representation, storage, retrieval, and manipulation of biomedical data. As part of the course, we will briefly examine the effect of programming paradigm choice on problem-solving approaches, and introduce data structures and algorithms. We will also examine knowledge representation schemes for capturing biomedical domain complexity and principles of data modeling for efficient storage and retrieval. The final project involves building a medical information system that encompasses the different concepts taught in the course.

Computer science basics covered in the first part of the course are integral to understanding topics covered in the latter part, and for completing the assigned homework.

This course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods.

The modern data analysis pipeline involves collection, preprocessing, storage, analysis, and interactive visualization of data.

The goal of this course, part of the Analytics: Essential Tools and Methods MicroMasters program, is for you to learn how to build these components and connect them using modern tools and techniques.

In the course, you’ll see how computing and mathematics come together. For instance, “under the hood” of modern data analysis lies  numerical linear algebra, numerical optimization, and elementary data processing algorithms and data structures. Together, they form the foundations of numerical and data-intensive computing.

The hands-on component of this course will develop your proficiency with modern analytical tools. You will learn how to mash up Python, R, and SQL through Jupyter notebooks, among other tools. Furthermore, you will apply these tools to a variety of real-world datasets, thereby strengthening your ability to translate principles into practice.

This course explores the role of computation in the conception and representation of shape and form. With a recognition that artists, architects and designers learn best when creating new work, programming will be taught as a creative medium. In the Python language, students will develop, analyze and critique algorithmic approaches to digital drawing, modeling, and projection. Specifically, the powerful, robust, and well-documented Python Rhinoscript library will be introduced and explored in detail. This API allows Rhinoceros modeling software to be scripted with text-based code. Scripting in this manner can automate existing processes and can lead to novel kinds, relationships, and orders of shape and form. Architects, sculptors, and any artists or designers interested in the either fabrication or communication of form and shape will recognize the importance of projection–the the transformation of three-dimensional geometry onto a two-dimensional picture plane, cut sheet, paper or screen. As a result, this course focuses not only on the generation of geometry, but the output of geometry. In parallel to extending students' technical proficiency, this course will touch on the conceptual and theoretical implications of algorithmic design. Each of the five lessons will build upon each other to develop an understanding of the Python language, algorithmic strategies, and digital geometric craft (the interrelated structures and topologies that make up digital models). Beginning with the most primitive geometric element–the point–the course will build curves in two and three dimensions, organize those curves to function as the input for methods that generate surfaces. Subsequently, the course will return to the realm of drawing as surfaces will be used to generate lines and curves in concert with orthographic and perspective projection.

Information Technology (IT) is everywhere. Every aspect of human activity depends on it. All IT processes, whether they drive mobile phones, the Internet, transportation systems, enterprise systems, publishing, social networks or any other application, rely on software.

In this new and improved version of the course, you will learn to write software with a progressive hint system for first time programmers. The core skill is programming; not just the ability to piece together a few “lines of code,” but writing quality programs, which will do their job right, and meet the evolving needs of their users. Anyone can write a program; this course teaches you to write good programs.

The course starts from the basics of computing and takes you through a tour of modern object-oriented programming, including classes, objects, control structures, inheritance, polymorphism, and genericity.

Throughout the course, you will have the opportunity to learn the principles of programming as well as the techniques for designing correct and reliable programs by using the Eiffel programming language and notation. You will be trying out example problems to provide your solution, and see it immediately compiled and tested from within your browser. To this end, we are using the Codeboard;web-based IDE, developed at the Chair of Software Engineering (ETH Zurich). 

Beyond programming, you will also get a glimpse at theoretical computer science, the set of mathematical techniques that underlie computation and makes today’s IT-based world possible.

In this third edition of the course we specifically focus on helping students with little or no programming experience. To this end, we have improved the introductory material about the Eiffel language, and we have implemented a progressive hint system students can use to get guidance on how to solve the programming exercises.

"Really good course. Followed it with a couple of experienced colleagues all of them having a computer science background. They really liked the concepts and programming in Eiffel a lot. Many thanks to the team making this course available! Can not wait to start with the advanced course!" --Previous CAMSx Participant

Previous edition course evaluation:

Overall course rating (1: worst grade, 6: best grade):

Grade Resp.   %Resp
1          1          2%
2          0          2%
3          3          6%
4          9        18%
5          20       40%
6          17       34%

Total respondents: 50
Average: 4.96

Computing is a science. It’s an art, at least for those who practice it well, and it still retains much of its magic.

Part 1 of this course introduced the fundamental concepts, preparing you for the more advanced topics covered in this course!

Throughout this course, you will learn programming concepts and techniques, and practice them immediately through advanced browser-based tools that let you write programs, compile, run  and test them against predefined test sets. You will see your results right away in your browser!

In this Part 2, we will discuss:

• A major programming technique, inheritance, including Multiple inheritance: combining complementary abstractions
• Selective exports for solid modular design of large systems
• Functional programming with agents: going one level of abstraction higher or more
• Concurrency: how to build applications that, so to speak, walk and chew gum at the same time
• Design patterns: how you can benefit from the best architectural practices of the industry, ironed out over many decades
• What makes a loop do its job right, with the notion of loop invariant
• Important practical examples: how to write an interactive application with undo-redo, and the example of topological sort
• Software engineering-- the construction not just of individual programs but also of complex, ambitious software systems

Join us in this computing and programming course to better understand the power and beauty of modern computer programming.

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