This is the first course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641.
Please note that this is first course is different in structure compared to most Udacity CS courses. There is a final project at the end of the course, and there are no programming quizzes throughout this course.
This course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff.
Supervised Learning is an important component of all kinds of technologies, from stopping credit card fraud, to finding faces in camera images, to recognizing spoken language. Our goal is to give you the skills that you need to understand these technologies and interpret their output, which is important for solving a range of data science problems. And for surviving a robot uprising.
Series Information: Machine Learning is a graduate-level series of 3 courses, covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.
- Machine Learning 1: Supervised Learning (this course)
- Machine Learning 2: Unsupervised Learning
- Machine Learning 3: Reinforcement Learning
If you are new to Machine Learning, we recommend you take these 3 courses in order.
The entire series is taught as a lively and rigorous dialogue between two eminent Machine Learning professors and friends: Professor Charles Isbell (Georgia Tech) and Professor Michael Littman (Brown University).
Why Take This Course?
In this course, you will gain an understanding of a variety of topics and methods in Supervised Learning. Like function approximation in general, Supervised Learning prompts you to make generalizations based on fundamental assumptions about the world.
Michael: So why wouldn't you call it "function induction?"
Charles: Because someone said "supervised learning" first.
Topics covered in this course include: Decision trees, neural networks, instance-based learning, ensemble learning, computational learning theory, Bayesian learning, and many other fascinating machine learning concepts.
In your final project, you will explore important techniques in Supervised Learning, and apply your knowledge to analyze how algorithms behave under a variety of circumstances.
Prerequisites and Requirements
A strong familiarity with Probability Theory, Linear Algebra and Statistics is required. An understanding ofIntro to Statistics, especially Lessons 8, 9 and 10, would be helpful.
Students should also have some experience in programming (perhaps through Introduction to CS) and a familiarity with Neural Networks (as covered in Introduction to Artificial Learning).
See the Technology Requirements for using Udacity