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XuetangX

Principles and Practice of Artificial Intelligence

Southwest Jiaotong University via XuetangX

Overview






In July 2017, China’s State Council released the "New Generation Artificial Intelligence Development Plan," elevating AI development to a national strategic priority. As it fosters high-quality development, AI continues to enhance the concept of new productive forces, becoming a key indicator of a nation's level of technological innovation and high-end manufacturing. 

 

The "Artificial Intelligence" general education course offered by the School of Computer and Artificial Intelligence at Southwest Jiaotong University is designed to introduce students to the cutting-edge technology driving the Fourth Industrial Revolution.


Launched in 2017, this course was among the first "Artificial Intelligence" courses offered by universities across the country. The teaching team has been deeply involved in AI-related theories, technologies, and methods, as well as industrial applications, building extensive experience in both research and education.

 

The course focuses on machine learning, deep learning, cutting-edge technologies in large models, and ethical safety, aiming to develop a strong foundation in algorithms, expose students to emerging technologies, and cultivate ethical awareness. Through project-based, hands-on learning, students progressively grasp the core concepts and technologies of artificial intelligence while solving real-world problems, preparing them to become AI practitioners.

 




Syllabus

  • Chapter 1: Introduction
    • 1.1 Inroduction of Artifical Intelligence
    • 1.2 School of Artifical Intelligence
    • 1.3 Application Scenarios of Artificial Intelligence
    • 1.4 Ethical Issues in the Application of AI Technologies
  • Chapter 2: Machine Learning
    • 2.1 Introduction of Machine Learning
    • 2.2 Linear Regression Model
    • 2.3 Basics of Optimization
    • 2.4 Decision Trees
    • 2.5 Bayesian Classifier
    • 2.6 Artificial Neural Networks
    • 2.7 Ensamle Learning
  • Chapter 3: Deep Learning
    • 3.1 Brief Introduction of Deep Learning
    • 3.2 Convolutional Neural Networks
    • 3.3 Recurrent Neural Networks
    • 3.4 Generative Adversarial Networks
    • 3.5 Transformer
    • 3.6 Diffusion Model
    • 3.7 Deep Learning Optimiztion Algorithm
    • 3.8 Neural Network Structure Design
  • Chapter 4: AI Generation Content
    • 4.1 Brief Introductio of AIGC
    • 4.2 Prompt Learning
    • 4.3 Contextual Learning
    • 4.4 Reinforcement Learning with Human Feedback
    • 4.5 Multimodal Large Model
    • 4.6 Artificial Intelligence Agent
  • Final Test

    Taught by

    Fei Teng

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