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Machine Learning courses

1656 Courses

California Institute of Technology XuetangX

人工智能基础

本课程全面介绍了人工智能的基础原理，涵盖四大方面：搜索与问题求解、知识与推理、学习与发现以及具体应用领域。搜索与问题求解涉及问题解决的基本原理、策略与图搜索以及博弈；知识与推理涵盖谓词逻辑、归结原理与确定性推理；学习与发现部分涉及机器学习知识，包括分类、回归和聚类算法；深度学习入门涉及图像识别、卷积神经网络、自然语言处理及循环神经网络。 通过本课程.
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California Institute of Technology XuetangX

大数据技术与应用

本课程以一系列大数据讲座为主线，突出实战性，激发学生学习兴趣和动力，促进学生理论与实践相结合，启发学生技术创新。注重结合应用实例融会贯通大数据中的理论方法和系统知识（平台、模块、工具），体会运用大数据技术解决实际问题的思路和效果。本课程兼顾信息类和非信息类学生。
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California Institute of Technology XuetangX

人工智能与创新

一场关于未来的革命正在悄然上演，创新已成为人工智能时代的主旋律。“人工智能与创新”课程基于POT-OBE和5E的育人理念和方法，面向所有非人工智能专业学生以及任何有兴趣驾驭AI去创新的学习者。课程目标在于培养能驾驭AI去创新的高手，而非AI专业人才，希望学习者能在这个飞速发展的时代中，主动拥抱AI，与之同行。 课程的第一部分为AI基础层，包括第1章和第2章，为学生推.
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California Institute of Technology XuetangX

机器人的主动感知与行为学习

了解机器人主动感知与行为学习，探索机器人如何主动感知其环境并通过学习机制获得新行为的中文讲座。研究关键概念，如机器人感知、感觉处理和行为适应，同时了解使机器人能够智能互动和通过基于经验的学习发展日益复杂能力的基本原理。
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California Institute of Technology XuetangX

金融科技模式与创新

本课程提供对金融科技领域最新模式和创新技术的深入理解，涵盖区块链技术、人工智能在金融服务中的应用、数字支付系统及大数据分析等前沿话题。学生通过案例分析，实现理论与实践结合，掌握金融科技的实际应用和发展趋势。非常适合对金融科技有兴趣的学生，助力在领域内取得突破和发展。 开课平台：XuetangX 分类：人工智能课程, 机器学习课程, 大数据课程, 金融科技课程,.
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California Institute of Technology XuetangX

2024清华高等教育论坛——“可能性的边界：人工智能赋能高等教育”

Visit XuetangX for more information Discover the exciting prospects at the 2024 Tsinghua Higher Education Forum, where experts will dive into the expansive role of artificial intelligence in transforming higher education. Engage in stimulating discussions that aim to redefine educational boundaries, elevate teaching methods, and revolutionize l.
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California Institute of Technology XuetangX

AI TIME ACL 预讲会

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California Institute of Technology XuetangX

AI TIME PhD ICLR专场六

Delve into the forefront of artificial intelligence and machine learning research during the AI TIME PhD ICLR专场六 session. This expertly curated event brings together leading minds in academia and industry to explore and discuss cutting-edge topics, all delivered in Mandarin. Attendees will gain valuable insights into recent advancements in ne.
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California Institute of Technology XuetangX

人工智能原理

人工智能是全球知名大学计算机专业的核心课程之一，也是国际上顶尖高校和研究机构的关键研究领域。该领域研究如何通过计算机软硬件实现Agent的感知、决策和智能行为，涵盖的理论基础包括搜索、推理、规划和学习，其应用范围涉及计算机视觉、图像分析、模式识别、专家系统、自动规划、智能搜索、计算机博弈、智能控制、机器人技术、自然语言处理、社交网络、数据挖掘和虚拟.
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California Institute of Technology XuetangX

计算机是如何实现智能的

Join a captivating Chinese language lecture by Tsinghua University, delving into how computers achieve intelligence. This course uncovers the foundational concepts of artificial intelligence and computer intelligence, highlighting the processes of information processing, decision making, and the simulation of human cognitive functions. Learn.
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More and more products are now being developed using artificial intelligence. To avoid being left on the sidelines of progress, managers must understand how the robot’s “brains” work

Artificial intelligence (AI) and machine learning technologies have been used for many years, but now the intensity of their use has increased significantly. For example, machine learning is being actively implemented in telecommunications, retail, marketing and e-commerce. But many still do not fully understand what it is.

Machine learning involves the system processing a large number of examples, during which it identifies patterns and uses them to predict the characteristics of new data. In other words, this is the process of giving AI ml courses “consciousness”, the ability to remember and analyze.

Machine learning use cases

The use of machine learning has touched many areas in our lives. Let's look at the most striking examples of the use of computer intelligence:

Facial recognition in the subway will help identify violators or criminals in a huge mass of people. Ordinary observers cannot cope with this task. But a fast-learning machine will do this job without any problems.

What do you need for machine learning (ML)?

For those interested in training, there are several requirements to be met in order to be successful in this field. So, there are the main points you need to know about the machine learning course. These requirements include:

  1. Basic knowledge of programming languages such as Python, R, Java, JavaScript, etc.

  2. Average knowledge of statistics and probability.

  3. Basic knowledge of linear algebra in the ml course. In a linear regression model, a line is drawn through all the data points, and that line is used to calculate new values.

  4. Understanding Calculus.

  5. Knowledge of how to clean and structure raw data into the desired format to reduce the time required for decision making.

Machine learning courses from AI Eeducation are the best choice!