|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-artificial-intelligence-ai-o/
课程评论：没有评论
《人工智能（AI）导论》课程概述： 在当今数据爆炸的时代，大量非结构化数据（如表格、图像和视频）需要被分析。尤其在疫情期间，对戴口罩合规性的识别等图像分析需求愈发迫切。本课程旨在帮助学员理解如何利用各种人工智能技术分析海量非结构化和半结构化数据，从而解读其含义。 课程内容包括： * **什么是人工智能（AI）？** * **AI的主要能力** * **各类AI技术及其应用场景** * **AI解决方案的构成要素** * **如何规划AI项目和选择相关技术** 课程将分为多个部分： 1. **AI概述及四大能力层级介绍**： * **感知（Sensing）**：涵盖语音、图像、文本和传感器识别等感知能力，以及相关技术和案例分析。 * **学习（Learning）**：探讨机器学习（监督学习、无监督学习、强化学习）在模型改进中的作用，并进行案例分析。 * **推理（Reasoning）**：展示语义知识表示在推理能力开发中的作用，并进行案例分析。 * **交互（Interaction）**：介绍人机协作中的应用，以及相关技术特点和案例分析。 2. **四大能力（感知、自适应学习、语义知识表示、协作）的综合应用**：展示这些能力如何共同驱动各种日常应用场景。 3. **总结**：回顾课程内容，并提供推荐阅读材料。 本课程穿插有互动小测验，以巩固学习效果。
As we deal with current data explosive world, much of the data is unstructured - forms, tables, images, and video. As we deal with social interactions in Covid-19, compliance for mask wearing gets added to a number of other image analysis problems.We have a strong need to analyze large set of unstructured and semi-structured data to interpret the meaning using various AI technology. What are the different types of AI capabilities and associated technologies? How do you select an AI use case and associated technology.In this course, you will understandWhat is AI?Major capabilities of AIVarious AI technologies and associated use casesComponents of an AI solutionStrategize an AI engagement and associated technologiesThis course is divided into multiple sections. After this introductory section,We will cover what is AI and four major tiers of AI capabilities. In each area, we will identify key technologies and how they drive and transform analytics.First area is sensing - this includes perception capabilities embedded in our ingestion of speech, images, text, and sensors. We will cover this technology and will also include one case study in this area.Second area is learning - here we discuss the role of adaptive learning in model improvement as seen today in supervised, unsupervised and reinforcement learning. We will cover this technology and will also include one case study in this area.Third area is reasoning - our discussion here showcases the role of semantic knowledge representation in developing reasoning capabilities. We will cover this technology and will also include one case study in this area.Four area is interaction - it covers our use of collaboration in human - machine interaction. We will define key characteristics of this technology and will also include one case study in this area.Next, we will round up the four capabilities - perception, adaptive learning, semantic knowledge representation and collaboration and show how they have collectively shaped various common life use casesIn last summary section, we will review our findings and provide a set of recommended readings.The course will cover many interactive quizzes to test your understanding on the subject.
