教学大纲Chinese Traditional Culture
Chu Culture
Introduction to Behavioral Economics
Introduction to Statistics and Applications within Data Science
Healthy China Initiative and International Health Cooperation
Intelligent Robot and Advanced Manufacturing
Smart Earth
Environment and Social Development
Infections and Immune Response
Artificial Intelligence and Big Data
Critical Conservation and Revitalization of Architecture Heritage
选课操作指南
The Outline of Artificial Intelligence and Big Data
1. Basic Teaching Information
Course Code： |
Course Title：Artificial Intelligence and Big Data |
||
Faculty：School of Electronic Information |
Targeted Student：Freshman |
||
Course Credit：1 |
Lecture Hours：20 （consisted of __20___ theoretical hours and __0___ practical hours） |
||
Course Leader： |
Name：Marcello Pelillo |
E-mail：marcello.pelillo@gmail.com |
|
Office： |
Mobile： |
||
Course Staff： |
Name：Haijian Zhang |
E-mail：haijian.zhang@whu.edu.cn |
|
Office：606 |
Mobile：13545195918 |
||
Course Type：（公共基础课程Public Basic Course/基础通识课程Basic General Course/核心通识课程Core General Course/一般通识课程General Course/大类平台课程Generic Platform Course/专业必修课Compulsory Specialty Course/专业选修课程Optional Specialty Course） |
|||
Related Preview Courses： |
|||
2. Course Introduction（no more than 500 words）
Artificial intelligence (AI) requires a large amount of data as the basis for "thinking" and "decision-making". Big data also requires AI technology for data value operations. One of the main channels for data application is the artificial intelligence product, i.e., the agent. The greater the amount of data provided to the agent, the better the operation effect of the agent will be. The agent needs a lot of data for "training" and "validation", so as to ensure the reliability and stability of operation. This course introduces the basic theories, methods and applications of artificial intelligence and big data.
3. The Allocation of Content and Lecture Hours
Content |
Lecture Hours |
Introduction |
2 |
Artificial Intelligence |
2 |
Big Data and Intelligent Data Processing |
2 |
Neural Networks |
2 |
Deep Learning |
2 |
Computer Vision（1） |
2 |
Computer Vision（2） |
2 |
Implementations |
2 |
Applications（1） |
2 |
Applications（2） |
2 |
4. Assessment Methods and Marking Criterion
2 regular homework (40%) + 1 course report (60%)
5. Textbooks and References
[1] Anand Deshpande, Manish Kumar, 《Artificial Intelligence for Big Data: Complete guide to automating Big Data solutions using Artificial Intelligence techniques》, Packt Publishing, 2018.
[2] Fernando Iafrate, 《Artificial Intelligence and Big Data: The Birth of a New Intelligence》, Wiley, 2018.
