Learn about how to make mobile robots move in effective, safe, predictable, and collaborative ways using modern control theory.
Computer Science: Artificial Intelligence, Robotics, Vision
Discover the world of mobile robots - how they move, how they interact with the world, and how to build them!
Dieser Kurs vermittelt einen Überblick über die Grundlagen des Maschinellen Sehens an Hand der Extraktion von 3D-Information aus dem Stereokamerabild einer Szene.
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.
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.
Explore key ideas in game design, programming, architecture, game engines, player experience and game AI.
Understanding how the brain works is one of the fundamental challenges in science today. This course will introduce you to basic computational techniques for analyzing, modeling, and understanding the behavior of cells and circuits in the brain.
We will present the state of the art energy minimization algorithms that are used to perform inference in modern artificial vision models: that is, efficient methods for obtaining the most likely interpretation of a given visual input. We will also cover the popular max-margin framework for estimating the model parameters using inference.
Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。本課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。]
The activity of neurons in the brain and the code used by these neurons is described by mathematical neuron models at different levels of detail.
Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
This course will use social network analysis, both its theory and computational tools, to make sense of the social and information networks that have been fueled and rendered accessible by the internet.
An introduction to network analysis and statistical methods used in contemporary Systems Biology and Systems Pharmacology research.
Learn about General Game Playing (GGP) and develop GGP programs capable of competing against humans and other programs in GGP competitions.
This course is an introduction to linear algebra. It has been argued that linear algebra constitutes half of all mathematics.
Networked Life will explore recent scientific efforts to explain social, economic and technological structures -- and the way these structures interact -- on many different scales, from the behavior of individuals or small groups to that of complex networks such as the Internet and the global economy.
This course introduces the concepts, applications, algorithms, programming, and design of recommender systems--software systems that recommend products or information, often based on extensive personalization. Learn how web merchants such as Amazon.com personalize product suggestions and how to apply the same techniques in your own systems!
Find out how modern electronic markets work, why stock prices change in the ways they do, and how computation can help our understanding of them. Build algorithms and visualizations to inform investing practice.
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.
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.