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 apply these technologies and interpret their output, which is important for solving a range of data science problems. And for surviving a robot uprising.
You can expect to spend 8-10 hours per week on this course.
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).
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.
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.