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.
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.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
Vinayak Mehtaaudited this course, spending 10 hours a week on it and found the course difficulty to be hard.
The way in which the instructors teach is awesome.
This is a masters level machine learning course. I would recommend taking this course at a slow pace if you're a beginner in the machine learning domain, making sure that you get a thorough understanding of the material.