Machine Learning

Pedro Domingos, University of Washington

Why write programs when the computer can instead learn them from data? In this class you will learn how to make this happen, from the simplest machine learning algorithms to quite sophisticated ones. Enjoy!

Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective when manual programming is not. Machine learning (also known as data mining, pattern recognition and predictive analytics) is used widely in business, industry, science and government, and  there is a great shortage of experts in it. If you pick up a machine learning textbook you may find it forbiddingly mathematical, but in this class you will learn that the key ideas and algorithms are in fact quite intuitive. And powerful!
Most of the class will be devoted to supervised learning (in other words, learning in which a teacher provides the learner with the correct answers at training time). This is the most mature and widely used type of machine learning. We will cover the main supervised learning techniques, including decision trees, rules, instances, Bayesian techniques, neural networks, model ensembles, and support vector machines. We will also touch on learning theory with an emphasis on its practical uses. Finally, we will cover the two main classes of unsupervised learning methods: clustering and dimensionality reduction. Throughout the class there will be an emphasis not just on individual algorithms but on ideas that cut across them and tips for making them work.
In the class projects you will build your own implementations of machine learning algorithms and apply them to problems like spam filtering, clickstream mining, recommender systems, and computational biology. This will get you as close to becoming a machine learning expert as you can in ten weeks!

Syllabus

Week One: Basic concepts in machine learning.Week Two: Decision tree induction.Week Three: Learning sets of rules and logic programs.Week Four: Instance-based learning.Week Five: Statistical learning.Week Six: Neural networks.Week Seven: Model ensembles.Week Eight: Learning theory.Week Nine: Support vector machines.Week Ten: Clustering and dimensionality reduction.

Recommended Background

The main prerequisite for this class is basic knowledge of programming. Some previous exposure to probability, statistics, linear algebra, calculus and/or logic is useful but not essential.

Suggested Readings

The class is self-contained, but a good complement to it is the book The Master Algorithm, by Pedro Domingos, published by Basic Books. For a more technical treatment, the textbook Machine Learning, by Tom Mitchell, published by McGraw-Hill, covers most but not all of the topics in the class. The remaining topics can be found in Pattern Classification (second edition), by Duda, Hart and Stork (Wiley), and other textbooks.

Course Format

The class will consist of a series of lecture videos, typically 5 to 15 minutes in length. Each video contains a few integrated quiz questions. There will also be standalone homeworks that are not part of video lectures, programming assignments, and a final exam.

FAQ

  • Will I get a certificate after completing this class?
Yes. Students who successfully complete the class will receive a certificate signed by the instructor.

  • What resources will I need for this class? 
You will need access to a computer with a compiler/environment for the programming language of your choice. 
  • What is the coolest thing I'll learn if I take this class?
Machine learning is the scientific method on steroids. It follows the same process of generating, testing, and discarding or refining hypotheses. But, while a scientist may spend her whole life coming up with and testing a few hundred hypotheses, a machine learning system can do the same in a fraction of a second.
Dates:
  • Date to be announced, 10 weeks
Course properties:
  • Free:
  • Paid:
  • Certificate:
  • MOOC:
  • Video:
  • Audio:
  • Email-course:
  • Language: English Gb

Reviews

No reviews yet. Want to be the first?

Register to leave a review

Show?id=n3eliycplgk&bids=695438
Included in selections:
Small-icon.hover Machine Learning
Machine learning: from the basics to advanced topics. Includes statistics...
NVIDIA
More on this topic:
18-465s07 Topics in Statistics: Statistical Learning Theory
The main goal of this course is to study the generalization ability of a number...
30106_6d39_5 Hilary Mason: Advanced Machine Learning by O&'Reilly Media
Apply best practices to common types of machine learning problems, extract...
9ytiudz0qyhwumjftxcnxzn4fctgw6zszm7aj5s7mxhhxsapikypl08vpweghajf2qmuhpiycu2q3knew7w=s0#w=1725&h=1060 Intro to Java Programming. Building Programs with Classes & Objects
Learn essential computer science and object-oriented programming concepts in...
Classlogo Algorithmic Thinking
Experienced Computer Scientists analyze and solve computational problems at...
Download?download_frd=1&verifier=s1ysfwv3ocxq9f45lq4nseuhm3setscwvng0oqgu Customer Segmentation: A Scientific Approach to Marketing
Marketing isn’t all business. It’s also an art—and definitely a science (especially...
More from 'Computer Science':
695ff980-b45a-425f-bee6-51bf6e962d90-de2d1a1c22e0.small Video Game Design History
Learn about the evolution of video games from experts at The Strong National...
595aa0b6-077d-439b-a651-95a9ee65c51a-fc966dc2648f.small Video Game Design and Balance
Learn about the video game design process and experiment with effective methods...
Fcd236ea-68ae-46f7-b991-849a41cebc64-0ea84acf6bad.small Video Game Asset Creation and Process
Learn about the tools, processes and platforms that allow video game assets...
Regular_7e290d30-8e84-46b2-bf50-801246fb157c Advanced Data Mining with Weka
Learn how to use popular packages that extend Weka's functionality and areas...
Regular_0b883f52-bc27-40f6-b633-d5fa9dd1101a Prepare to Run a Code Club
Build your confidence and get practical advice on launching and running a Code...
More from 'Coursera':
Success-from-the-start-2 First Year Teaching (Secondary Grades) - Success from the Start
Success with your students starts on Day 1. Learn from NTC's 25 years developing...
New-york-city-78181 Understanding 9/11: Why Did al Qai’da Attack America?
This course will explore the forces that led to the 9/11 attacks and the policies...
Small-icon.hover Aboriginal Worldviews and Education
This course will explore indigenous ways of knowing and how this knowledge can...
Ac-logo Analytic Combinatorics
Analytic Combinatorics teaches a calculus that enables precise quantitative...
Talk_bubble_fin2 Accountable Talk®: Conversation that Works
Designed for teachers and learners in every setting - in school and out, in...

© 2013-2019