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Harbin Institute of Technology

Mathematical Algorithm in AI

Harbin Institute of Technology via XuetangX

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Overview

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Focus on teaching the mechanisms of artificial intelligence algorithms, cultivate students' awareness of independent AI learning at the current stage, equip students with a solid theoretical foundation in artificial intelligence and the ability to solve practical problems with relevant methodologies, and lay a foundational knowledge of AI algorithms for their major studies.

Syllabus

  • Mathematical Algorithm in AI Introduction
    • Chapter 1 What is an Ill-posed Inverse Problem Overview and Electrical Engineering Motivation
      • 1.1 What is an Ill-posed Inverse Problem Overview and Electrical Engineering Motivation（Ⅰ）
      • 1.2 What is an Ill-posed Inverse Problem Overview and Electrical Engineering Motivation（Ⅱ）
      • 1.3 What is an Ill-posed Inverse Problem Overview and Electrical Engineering Motivation（Ⅲ）
    • Chapter 2 Linear Integral Equations of the First Kind Fredholm and Volterra Theory
      • 2.1 Linear Integral Equations of the First Kind Fredholm and Volterra Theory（Ⅰ）
      • 2.2 Linear Integral Equations of the First Kind Fredholm and Volterra Theory（Ⅱ）
      • 2.3 Linear Integral Equations of the First Kind Fredholm and Volterra Theory（Ⅲ）
      • 2.4 Linear Integral Equations of the First Kind Fredholm and Volterra Theory（Ⅳ）
    • Chapter 3 Functional Analysis Tools Norms, Inner Products, Compact Operators, and Delta Functions
      • 3.1 Functional Analysis Tools Norms, Inner Products, Compact Operators, and Delta Functions（Ⅰ）
      • 3.2 Functional Analysis Tools Norms, Inner Products, Compact Operators, and Delta Functions（Ⅱ）
      • 3.3 Functional Analysis Tools Norms, Inner Products, Compact Operators, and Delta Functions（Ⅲ）
    • Chapter 4 Frequency Domain Methods for Inverse Problems in DSP
      • 4.1 Frequency Domain Methods for Inverse Problems in DSP（Ⅰ）
      • 4.2 Frequency Domain Methods for Inverse Problems in DSP（Ⅱ）
      • 4.3 Frequency Domain Methods for Inverse Problems in DSP（Ⅲ）
      • 4.4 Frequency Domain Methods for Inverse Problems in DSP（Ⅳ）
    • Chapter 5 Professor Speech Script
      • 5.1 Professor Speech Script（Ⅰ）
      • 5.2 Professor Speech Script（Ⅱ）
      • 5.3 Professor Speech Script（Ⅲ）
      • 5.4 Professor Speech Script（Ⅳ）
      • 5.5 Professor Speech Script（Ⅴ）
    • Chapter 6 Forecasting as an Inverse Problem Why It Is Ill-Posed
      • 6.1 Forecasting as an Inverse Problem Why It Is Ill-Posed（Ⅰ）
      • 6.2 Forecasting as an Inverse Problem Why It Is Ill-Posed（Ⅱ）
      • 6.3 Forecasting as an Inverse Problem Why It Is Ill-Posed（Ⅲ）
      • 6.4 Forecasting as an Inverse Problem Why It Is Ill-Posed（Ⅳ）
      • 6.5 Forecasting as an Inverse Problem Why It Is Ill-Posed（Ⅴ）
      • 6.6 Forecasting as an Inverse Problem Why It Is Ill-Posed（Ⅵ）
    • Chapter 7 Stable Forecasting in Practice Case Studies from Power and EnergySystems
      • 7.1 Stable Forecasting in Practice Case Studies from Power and EnergySystems（Ⅰ）
      • 7.2 Stable Forecasting in Practice Case Studies from Power and EnergySystems（Ⅱ）
      • 7.3 Stable Forecasting in Practice Case Studies from Power and EnergySystems（Ⅲ）
    • Chapter 8 Inverse Problems for Energy Community Mathematical Modeling
      • 8.1 Inverse Problems for Energy Community Mathematical Modeling（Ⅰ）
      • 8.2 Inverse Problems for Energy Community Mathematical Modeling（Ⅱ）
      • 8.3 Inverse Problems for Energy Community Mathematical Modeling（Ⅲ）
    • Chapter 9 Robust Numerical Methods for BoundaryValue Problems
      • 9.1 Robust Numerical Methods for BoundaryValue Problems（Ⅰ）
      • 9.2 Robust Numerical Methods for BoundaryValue Problems（Ⅱ）
    • Final Examination

      Taught by

      LiGuo Wang, Denis Sidorov, and Aliona Dreglea

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