Online courses directory (6)
Computational thinking is an invaluable skill that can be used across every industry, as it allows you to formulate a problem and express a solution in such a way that a computer can effectively carry it out.
In this course, part of the Big Data MicroMasters program, you will learn how to apply computational thinking in data science. You will learn core computational thinking concepts including decomposition, pattern recognition, abstraction, and algorithmic thinking.
You will also learn about data representation and analysis and the processes of cleaning, presenting, and visualizing data. You will develop skills in data-driven problem design and algorithms for big data.
The course will also explain mathematical representations, probabilistic and statistical models, dimension reduction and Bayesian models.
You will use tools such as R, MOA and data processing libraries in associated language environments.
Today, computer graphics is a central part of our lives, in movies, games, computer-aided design, virtual reality, virtual simulators, visualization and even imaging products and cameras. This course, part of the Virtual Reality (VR) Professional Certificate program, teaches the basics of computer graphics that apply to all of these domains.
Students will learn to create computer-generated images of 3D scenes, including flybys of objects, make a real-time scene viewer, and create very realistic images with raytracing. We will start with a simple example of viewing a teapot from anywhere in space, understanding the basic mathematics of virtual camera placement. Next, you will learn how to use real-time graphics programming languages like OpenGL and GLSL to create your own scene viewer, enabling you to fly around and manipulate 3D scenes. Finally, we will teach you to create highly realistic images with reflections and shadows using raytracing.
This course runs for 6 weeks and consists of four segments. Each segment includes an individual programming assignment:
- Overview and Basic Math (Homework 0: 10% of grade)
- Transformations (Homework 1: 20% of grade)
- OpenGL and Lighting (Homework 2: 35% of grade)
- Raytracing (Homework 3: 35% of grade)
This term, students who earn a total score of 50% or greater will have passed the course and may obtain a certificate from UC San DiegoX.
This course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features updated lecture videos, lecture exercises, and problem sets to use the new version of Python 3.5. Even if you took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning.
Since these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not "computation appreciation" courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will.
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.
Microfabrication and nanofabrication are the basis of manufacturing for nearly all modern miniaturized systems that are ubiquitously used in our daily life. Examples include; computer chips and integrated sensors for monitoring our environment, cars, mobile phones, medical devices and more.
Micro- and nanofabrication can be taught to students and professionals by textbooks and ex-cathedra lectures, but the real learning comes from seeing the manufacturing steps as they happen.
In this engineering course, we will go a step beyond classroom teaching to not only explain the basics of each fabrication step but also show you how it’s done through video sequences and zooming into the equipment.
The increased demand by consumers and businesses for more utility, connectivity and smarter and more efficient electronic technology not only creates a need for more embedded systems but also for engineers in the embedded systems field.
In this lab-based computer science course, explore the complexities of embedded systems and learn how to develop your own real-time operating system (RTOS) by building a personal fitness device with Bluetooth connectivity (BLE). An operating system (OS) is a software system that computers use to manage the resources of a computer. The OS decides which tasks are performed when and decides how resources are utilized. Simple embedded systems, which are a combination of electrical, mechanical, chemical, and computer components designed to perform a dedicated function, originally did not need an OS. However, as embedded systems have evolved, so have their complexities. To manage this, an RTOS is now required.
Embedded systems are often deployed in safety-critical situations such as automotive, military, industrial, and medical applications. In applications such as communications and consumer electronics, response time and processing speed are important. A real-time system not only needs to arrive at the correct answer, but must also get the correct answer at the correct time. A RTOS manages a computer's resources so that tasks are performed in a timely mannner.
In this computer science course, students will learn the design fundamentals of an RTOS from the bottom up and use these fundamentals to build practical real-time applications. We will provide a board support package (BSP), so students will be able to focus on the RTOS and Bluetooth network without needing prior experience in circuits and I/O device driver software. This is a hands-on project-based lab course, where you will incrementally build a personal fitness device with Bluetooth connectivity.
This course is intended for students and professional engineers wishing to improve their skills in the fields of embedded systems, product development, computer architecture, operating systems, and Bluetooth networks.
To complete this course, you will need to purchase a lab kit including a microcontroller board, an I/O board, and a Bluetooth module. Instructions about purchasing the kit and installing required software are at http://edx-org-utaustinx.s3.amazonaws.com/UT601x/RTOS.html .
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