Computer Vision & Machine Learning

Best Paper Awards in Computer Science (since 1996)(link)

Jeff Huang

作者 Jeff Huang 来自华盛顿大学,他收集了自1996年开始的各项计算机会议最佳论文,感谢无私奉献。


Computer Vision: From 3D Reconstruction to Visual Recognition(link)

Fei-fei Li, Silvio Savarese

This course delivers a systematic overview of computer vision, emphasizing two key issues in modeling vision: space and meaning. We will study the fundamental theories and important algorithms of computer vision together, starting from the analysis of 2D images, and culminating in the holistic understanding of a 3D scene.

斯坦福: 机器人学(link)

Oussama Khatib

涉及内容:机器人运动学、动力学、控制、运动规划、编程及涉及等。 Topics: robotics foundations in kinematics, dynamics, control, motion planning, trajectory generation, programming and design.

Machine Learning(link)(link2)(中文2)(视屏)

Andrew Ng

In this course, you'll learn about some of the most widely used and successful machine learning techniques. You'll have the opportunity to implement these algorithms yourself, and gain practice with them. You will also learn some of practical hands-on tricks and techniques (rarely discussed in textbooks) that help get learning algorithms to work well. This is an "applied" machine learning class, and we emphasize the intuitions and know-how needed to get learning algorithms to work in practice, rather than the mathematical derivations.

Familiarity with programming, basic linear algebra (matrices, vectors, matrix-vector multiplication), and basic probability (random variables, basic properties of probability) is assumed. Basic calculus (derivatives and partial derivatives) would be helpful and would give you additional intuitions about the algorithms, but isn't required to fully complete this course.

加州理工: Machine Learning(link)

Yaser S. Abu-Mostafa

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by graduate and undergraduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion, with the main topics listed below.

GPU Parallel Programming(link)

David Luebke, John Owens, Mike Roberts, Cheng-Han Lee

Learn the fundamentals of parallel computing with the GPU and the CUDA programming environment! In this class, you'll learn about parallel programming by coding a series of image processing algorithms, such as you might find in Photoshop or Instagram. You'll be able to program and run your assignments on high-end GPUs, even if you don't own one yourself.