About this course: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Course Creators and Instructors
Course Developer, Instructor (Summer 2015)
The twin goals of knowledge-based artificial intelligence (AI) are to build AI agents capable of human-level intelligence and gain insights into human cognition.
The learning goals of the Knowledge-Based AI course are to develop an understanding of (1) the basic architectures, representations and techniques for building knowledge-based AI agents, and (2) issues and methods of knowledge-based AI. The main learning strategies are learning-by-example and learning-by-doing. Thus, the course puts a strong emphasis on homework assignments and programming projects. The course will cover three kinds of topics: (1) core topics such as knowledge representation, planning, constraint satisfaction, case-based reasoning, knowledge revision, incremental concept learning, and explanation-based learning, (2) common tasks such as classification, diagnosis, and design, and (3) advanced topics such as analogical reasoning, visual reasoning, and meta-reasoning.
View the CS 7637 - Knowledge-Based AI Syllabus for more detail.
A good course on computer programming such as CS 1332 or Udacity’s CS 101 is beneficial for students. An introductory course on Artificial Intelligence, such as Georgia Tech's CS 3600 or CS 6601, is recommended but not required.
To succeed in this course, you should be able to answer 'Yes' to the following four questions:
- Are you comfortable with computer programming?
- Are you familiar with concepts of data structures and object-oriented programming, such as inheritance and polymorphism?
- Are you familiar with concepts of algorithms, such as sorting and searching algorithms?
- Are you confident with either Java or Python?
For more information about grading, course requirements, and more please refer to the CS 7637 course syllabus.
Required Course Readings
There are no required course readings. However, there will be recommended supplemental readings for each lesson in this course. Here are a few general-purpose readings in knowledge-based artificial intelligence:
- Artificial Intelligence: Structures and Strategies for Complex Problem Solving. George Luger. Sixth Edition. Pearson Education, 2009.
- Introduction to Knowledge Systems. Mark Stefik. Morgan Kauffman 1995.
- Artificial Intelligence. Patrick Winston. Third Edition. MIT Press 1993. Available online.
Minimum Technical Requirements
- Browser and connection speed: An up-to-date version of Chrome or Firefox is strongly recommended. We also support Internet Explorer 9 and the desktop versions of Internet Explorer 10 and above (not the metro versions). 2+ Mbps recommended; at minimum 0.768 Mbps download speed
- Operating system: - PC: Windows XP or higher with latest updates installed - Mac: OS X 10.6 or higher with latest updates installed - Linux: Any recent distribution that has the supported browsers installed
All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code.