课程号：CS206009

课程名：机器学习与计算视觉

英文课程名：Machine Learning and Computer Vision

开课单位：计算机科学与技术学院

授课教师：Marcello Pelillo

责任教师：苗启广   袁景

学分/学时：1学分/16学时

教师简介：Marcello Pelillo教授是意大利威尼斯大学计算机科学教授，担任欧洲生活技术中心主任，于1995年建立并领导其中的CVPR组。曾在耶鲁大学(美国)、麦吉尔大学(加拿大)、维也纳大学(奥地利)、约克大学(英国)、伦敦大学学院(英国)和澳大利亚国家信息通信技术学院(澳大利亚)担任访问客座教授。组织并主持了多个国际会议(EMMCVPR、IWCV、SIMBAD)，并于2017年担任计算视觉三大会议之一的 ICCV 的大会主席。

Marcello Pelillo is a Full Professor of Computer Science at Ca’ Foscari University, Venice, where he leads the Computer Vision and Pattern Recognition Lab, which he established in 1995. He has been the Director of the European Centre for Living Technology (ECLT) and has held visiting research/teaching positions at Yale University (USA), University College London (UK), McGill University (Canada), University of Vienna (Austria), York University (UK), NICTA (Australia), Wuhan University (China), Huazhong University of Science and Technology (Wuhan, China), South China University of Technology (Guangzhou, China). Prof. Pelillo has been elected Fellow of the IEEE and Fellow of the IAPR and is an IEEESMC Distinguished Lecturer.

课程简介：本课程的主要内容是向本科生讲授，特别是信息科学和人工智能等专业的本科学生，讲授当代机器学习理论的基本概念，以及讲解现代机器学习方法在计算视觉中的典型应用。课程的前两个部分介绍机器学习的基本理论工具和常用方法，涵盖监督学习与无监督学习的主要概念；第三部分介绍了现代机器学习方法在实际计算视觉问题中的典型应用。课程内容精选自IEEE和IAPR Fellow，Marcello Pelillo教授长期教学的教材与课件，该课程教学过程中使用的多种应用实例，包括图像分类、图像语义分割、目标识别与追踪等。

The objective of this course is to provide undergraduate students, especially the undergraduate students on information science and AI etc., with the fundamental concepts of modern machine learning theories and their typical applications in computer vision. The first two parts of this course introduce the basic theoretical toolkits and popular approaches in modern machine learning including supervised learning and unsupervised learning; the third part presents the typical applications of computer vision in practice. The lecture slides are well organized from the related teaching materials and courseware by Prof. Marcello Pelillo, fellow of both IEEE and IAPR. This course uses multiple real-world application examples including image classification, semantic segmentation and object detection, tracking and re-identification.

先修课程：线性代数

高等数学