Machine Learning  Georgia Tech
UdacityGratis
Informazione importanti
 Corso
 Online
 Quando:
Da definire
In this course, you'll learn how to apply Supervised, Unsupervised and Reinforcement Learning techniques for solving a range of data science problems.
Inizio  Luogo 

Da definire 
Online

Cosa impari in questo corso?
Neural Networks  ROX  B&B  SVM  
VC Dimensions 
Programma
Approx. 4 months
Built by Join thousands of students Course SummaryThis class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree.
Machine Learning is a graduatelevel course covering the area of Artificial Intelligence concerned with computer programs that modify and improve their performance through experiences.
The first part of the 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.
In part two, you will learn about Unsupervised Learning. Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? Such answers can be found in this section!
Finally, can we program machines to learn like humans? This Reinforcement Learning section will teach you the algorithms for designing selflearning agents like us!
Why Take This Course?You will learn about and practice a variety of Supervised, Unsupervised and Reinforcement Learning approaches.
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.
Closely related to pattern recognition, Unsupervised Learning is about analyzing data and looking for patterns. It is an extremely powerful tool for identifying structure in data. This section focuses on how you can use Unsupervised Learning approaches  including randomized optimization, clustering, and feature selection and transformation  to find structure in unlabeled data.
Reinforcement Learning is the area of Machine Learning concerned with the actions that software agents ought to take in a particular environment in order to maximize rewards. You can apply Reinforcement Learning to robot control, chess, backgammon, checkers, and other activities that a software agent can learn. Reinforcement Learning uses behaviorist psychology in order to achieve reward maximization. This section also includes important Reinforcement Learning approaches like Markov Decision Processes and Game Theory.
Prerequisites and RequirementsA strong familiarity with Probability Theory, Linear Algebra and Statistics is required. An understanding of Intro 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 Intelligence).
See the Technology Requirements for using Udacity.
Syllabus Supervised Learning Lesson 0: Machine Learning is the ROX
 Lesson 1: Decision Trees
 Lesson 2: Regression and Classification
 Lesson 3: Neural Networks
 Lesson 4: InstanceBased Learning
 Lesson 5: Ensemble B&B
 Lesson 6: Kernel Methods and Support Vector Machines (SVM)s
 Lesson 7: Computational Learning Theory
 Lesson 8: VC Dimensions
 Lesson 9: Bayesian Learning
 Lesson 10: Bayesian Inference
 Lesson 1: Randomized optimization
 Lesson 2: Clustering
 Lesson 3: Feature Selection
 Lesson 4: Feature Transformation
 Lesson 5: Information Theory
 Lesson 1: Markov Decision Processes
 Lesson 2: Reinforcement Learning
 Lesson 3: Game Theory
 Lesson 4: Game Theory, Continued