Machine Learning: Supervised Learning's Full Profile
Course Summary 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 suggest 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.
Days of the Week:
Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday
- Level of Difficulty: Intermediate
- Instructors: Pushkar Kolhe, Michael Littman, Charles Isbell
- Cost: $199 Per Month
- Institution: Udacity
Udacity was born out of a Stanford University experiment in which Sebastian Thrun and Peter Norvig offered their Introduction to Artificial Intelligence course online to anyone, for free. Over 160,000 students in more than 190 countries enrolled and not much later, Udacity was born. They are a growing team of educators and engineers on a mission to change the future of education. By making high-quality classes affordable and accessible for students across the globe: Udacity is democratizing education.
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