E.g., Friday, December 19, 2014
E.g., Friday, December 19, 2014
E.g., Friday, December 19, 2014
Self Paced

A real Caltech course, not a watered-down version. This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications.

8
Average: 8 (1 vote)
Self Paced

This course introduces the field of artificial intelligence (AI). Materials on AI programming, logic, search, game playing, machine learning, natural language understanding, and robotics introduce the student to AI methods, tools, and techniques, their application to computational problems, and their contribution to understanding intelligence.

0
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Self Paced Course - Start anytime

Can we program machines to learn like humans? This Reinforcement Learning course will teach you the algorithms for designing self-learning agents like us!

0
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Self Paced Course - Start anytime

Ever wonder how Netflix can predict what movies you'll like? Or how Amazon knows what you want to buy before you do? The answer can be found in Unsupervised Learning!

0
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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.

0
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Self Paced

Participants in this class will learn how to build, program, and control haptic devices, which are mechatronic devices that allow users to feel virtual or remote environments. In the process, participants will gain an appreciation for the capabilities and limitations of human touch, develop an intuitive connection between equations that describe physical interactions and how they feel, and gain practical interdisciplinary engineering skills related to robotics, mechanical engineering, electrical engineering, bioengineering, and computer science.

0
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This course is an introduction to linear algebra. It has been argued that linear algebra constitutes half of all mathematics.

0
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Apr 8th 2015

This course explores the basis of electronic sensing of our world and how we then use these measures to change it.

0
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Jan 21st 2015

Build real-world embedded solutions using a bottom-up approach from simple to complex in this hands-on, lab-based course.

0
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Feb 2nd 2015

Learn the concepts and methods of linear algebra, and how to use them to think about computational problems arising in computer science. Coursework includes building on the concepts to write small programs and run them on real data.

0
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Jan 12th 2015

The course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.

0
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Dec 23rd 2014

The course extends the fundamental tools in "Machine Learning Foundations" to powerful and practical models by three directions, which includes embedding numerous features, combining predictive features, and distilling hidden features. [這門課將先前「機器學習基石」課程中所學的基礎工具往三個方向延伸為強大而實用的工具。這三個方向包括嵌入大量的特徵、融合預測性的特徵、與萃取潛藏的特徵。]

4
Average: 4 (1 vote)
Jan 5th 2015

An introduction to network analysis and statistical methods used in contemporary Systems Biology and Systems Pharmacology research.

0
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Jan 11th 2015

This advanced course considers how to design interactions between agents in order to achieve good social outcomes. Three main topics are covered: social choice theory (i.e., collective decision making), mechanism design, and auctions.

0
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Jan 5th 2015

Explore key ideas in game design, programming, architecture, game engines, player experience and game AI.

8
Average: 8 (4 votes)
Jan 5th 2015

Discover the world of mobile robots - how they move, how they interact with the world, and how to build them!

8.33333
Average: 8.3 (3 votes)
Oct 29th 2014

本課程有兩大課程目標: 1. 使同學了解如何以搜尋達成人工智慧 2. 使同學能將相關技術應用到自己的問題上

0
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Self Paced Course - Start anytime

The Computer Science program will provide you with a breadth of experience in software, hardware, and mathematics. As a Computer Science Major, you will be required to complete a total of twenty-one courses.

7
Average: 7 (4 votes)

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