Upcoming Paid Online Courses (6)
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.
In the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life. We will examine real world examples of how analytics have been used to significantly improve a business or industry. These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. Through these examples and many more, we will teach you the following analytics methods: linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization. We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it will enable you to apply analytics to real-world applications.
The class will consist of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each. After each lecture piece, we will ask you a “quick question” to assess your understanding of the material. There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Each week will have a homework assignment that involves working in R or LibreOffice with various data sets. (R is a free statistical and computing software environment we’ll use in the course. See the Software FAQ below for more info). In the middle of the class, we will run an analytics competition, and at the end of the class there will be a final exam, which will be similar to the homework assignments.
Other topics covered in this software engineering course include:
- How to form, organize and manage small programming teams
- Introduction to design patterns: what they are and how to recognize opportunities to apply them
- Using Rails for more advanced features like third-party authentication and elegantly expressing design patterns that arise frequently in SaaS
There will be four homework assignments: two programming assignments, an open source assignment and one assignment about operations/deployment. There will also be several short quizzes. The videos and homework assignments used in this offering of the course were revised in October 2016.