This course involves multidisciplinary knowledge such as philosophy, brain science, mathematics, and computer. It is extensive, novel, and comprehensive. According to the types of artificial intelligence such as perceptual intelligence, cognitive intelligence, language intelligence, behavioral intelligence, etc.,it introduces the basic concepts, methods of machine learning, artificial neural networks, image processing, knowledge representation, natural language processing, robots, etc. and cutting-edge technologies of brain computer interface, brain-like computing of hybrid intelligence and brain-like intelligence. Combined with application cases in manufacturing, military, medical and other fields, it enables students of all majors to fully understand the basic principles and methods of artificial intelligence, and cultivates students’ artificial intelligence thinking and ability of using artificial intelligence methods to solve problems in practice. It lets students lay the foundation of learning and integration innovation for future artificial intelligence + professional .
Overview
Syllabus
- Chapter 1 Introduction
- 1.1 Life intelligence and artificial intelligence
- 1.2 The birth process of artificial Intelligence
- 1.3 Multidisciplinary crossover artificial intelligence
- 1.4 Big data concept
- 1.5 Big data value and cases
- 1.6 Research and application of artificial intelligence
- Chapter 2 Philosophical Basis of AI
- 2.1 Artificial intelligence under big history
- 2.2 The philosophical thinking about artificial intelligence
- 2.3 Artificial intelligence consciousness, mind, body and intelligence
- 2.4 The rational nature of artificial intelligence
- Chapter 3 Brain and Neural Science Basis
- 3.1 Brain structure and function
- 3.2 Brain nervous system
- 3.3 New finds of brain functions
- 3.4 Cognition and intelligence
- Chapter 4 Artificial Neural Networks
- 4.1 Overview of artificial neural network
- 4.2 Perceptron and feedforward neural network
- 4.3 BP algorithm and application
- 4.4 Convolutional neural network
- 4.5 Deep neural network
- Chapter 5 Machine Learning
- 5.1 Supervised learning
- 5.2 Basic theories and methods of deep learning
- 5.3 Deep learning applications
- 5.4 Reinforcement learning
- 5.5 Transfer learning
- 5.6 Machine game
- 5.7 Machine art creation
- 5.8 Machine intelligent design
- Chapter 6 Sense Intelligence
- 6.1 Digital image processing applications
- 6.2 Computer vision
- 6.3 Machine vision
- 6.4 Machine vision applications
- 6.5 Pattern recognition basics
- Chapter 7 Cognition Intelligence
- 7.1 Knowledge representation
- 7.2 Logic and reasoning
- 7.3 Fuzzy reasoning
- 7.4 Blind search method
- 7.5 Knowledge graph
- Chapter 8 Language Intelligence
- 8.1 General issues in natural language understanding
- 8.2 Intelligence Q & A
- 8.3 Speech recognition basics
- 8.4 Introduction to machine translation
- Chapter 9 Robotics
- 9.1 Industrial robots
- 9.2 Service robots
- 9.3 Robot vision
- 9.4 Mobile robot synchronous localization and map construction
- 9.5 Path planning for mobile robots
- Chapter 10 Hyrbrid Intelligence
- 10.1 Basic concepts of hybrid intelligence
- 10.2 Brain computer interface
- 10.3 Wearable technology
- 10.4 Exoskeleton technology
- Chapter 11 Brain-like Intelligence
- 11.1 Brain-like computing
- 11.2 Artificial brain
- Chapter 12 Artificial Intelligence and Society Development
- 12.1 From digital manufacturing to intelligent manufacturing
- 12.2 Intelligence medical
- 12.3 Application of artificial intelligence in the military
- 12.4 Intelligence city
- 12.5 Artificial intelligence ethics
- 12.6 Robot ethics
- 12.7 Artificial intelligence law
Taught by
Hongwei Mo
