Introduction to Artificial Intelligence Zhejiang University of Technology

Artificial intelligence is a rapidly developing emerging discipline and has become the core technology of many high-tech products. Artificial intelligence simulates human intelligence to solve problems and has very wide applications in almost all fields.
This course is an entry-level artificial intelligence course, suitable for beginners, and can help them learn artificial intelligence from scratch. This course adopts the professional textbook "Introduction to Artificial Intelligence" (4th Edition) compiled by Professor Wang Wanliang of Zhejiang University of Technology. It closely focuses on the basic ideas, basic theories, basic methods and applications of artificial intelligence, and integrates some cutting-edge aspects of artificial intelligence. content. This course has 12 lectures in total, including: overview of artificial intelligence, first-order predicate logic representation, production representation and frame representation, reasoning methods based on predicate logic, credibility methods and evidence theory, fuzzy reasoning methods, search and solution strategies , Genetic Algorithm and its Application, Ant Colony Algorithm and its Application, Expert System and Machine Learning, BP Neural Network and its Application and Hopfield Neural Network and its Application.

• Chapter 1：Introduction (2 minutes and 20 seconds)

• Chapter 2：smart concept (3 minutes and 15 seconds)

• Chapter 3：Intelligent characteristics (7 minutes and 57 seconds)

• Chapter 4：Definition of artificial intelligence (4 minutes and 33 seconds)

• Chapter 5：A brief history of the development of artificial intelligence (10 minutes and 31 seconds)

• Chapter 6：Basic content of artificial intelligence research (11 minutes and 31 seconds)

• Chapter 7：propositional logic (6 minutes and 29 seconds)

• Chapter 8：predicate (9 minutes and 15 seconds)

• Chapter 9：predicate formula (20 minutes and 49 seconds)

• Chapter 10：Properties of predicate formulas (7 minutes and 3 seconds)

• Chapter 11：First-order predicate logic knowledge representation (2 minutes and 27 seconds)

• Chapter 12：Characteristics of first-order predicate logic knowledge representation (2 minutes and 43 seconds)

• Chapter 13：production (11 minutes and 51 seconds)

• Chapter 14：production system (7 minutes and 50 seconds)

• Chapter 15：Examples of production systems (13 minutes and 26 seconds)

• Chapter 16：Characteristics of production notation (3 minutes and 56 seconds)

• Chapter 17：frame notation (11 minutes and 0 seconds)

• Chapter 18：Reasoning styles and their classification (2 minutes and 13 seconds)

• Chapter 19：reductive deductive reasoning (5 minutes and 31 seconds)

• Chapter 20：Predicate is formulated as clause set 1 (12 minutes and 42 seconds)

• Chapter 21：The predicate is formulated as clause set 2 (11 minutes and 27 seconds)

• Chapter 22：Robinson's principle of reduction (14 minutes and 29 seconds)

• Chapter 23：reduction inversion (12 minutes and 20 seconds)

• Chapter 24：Apply the principle of reduction to solve problems (7 minutes and 4 seconds)

• Chapter 25：uncertain reasoning (10 minutes and 25 seconds)

• Chapter 26：credibility approach (15 minutes and 25 seconds)

• Chapter 27：probability distribution function (11 minutes and 21 seconds)

• Chapter 28：Belief function_likelihood function (6 minutes and 27 seconds)

• Chapter 29：Reasoning based on evidence theory (12 minutes and 5 seconds)

• Chapter 30：Examples of reasoning based on evidence theory (9 minutes and 22 seconds)

• Chapter 31：fuzzy logic proposed (8 minutes and 24 seconds)

• Chapter 32：fuzzy set (4 minutes and 54 seconds)

• Chapter 33：The definition and representation of fuzzy sets (9 minutes and 37 seconds)

• Chapter 34：membership function (7 minutes and 28 seconds)

• Chapter 35：Fuzzy relations and their synthesis (11 minutes and 42 seconds)

• Chapter 36：Fuzzy reasoning (5 minutes and 3 seconds)

• Chapter 37：Fuzzy decision-making (7 minutes and 39 seconds)

• Chapter 38：Fuzzy inference application (7 minutes and 26 seconds)

• Chapter 39：Search concept (9 minutes and 44 seconds)

• Chapter 40：State space knowledge representation method (12 minutes and 7 seconds)

• Chapter 41：Heuristic graph search strategy—heuristic strategy (8 minutes and 22 seconds)

• Chapter 42：Heuristic graph search strategies—heuristic information and valuation functions (12 minutes and 57 seconds)

• Chapter 43：Heuristic graph search strategy—A search algorithm (5 minutes and 23 seconds)

• Chapter 44：Heuristic graph search strategy—A_search algorithm and its characteristics (4 minutes and 56 seconds)

• Chapter 45：basic genetic algorithm (17 minutes and 43 seconds)

• Chapter 46：coding (6 minutes and 51 seconds)

• Chapter 47：Scale transformation of fitness function (5 minutes and 43 seconds)

• Chapter 48：choose (8 minutes and 5 seconds)

• Chapter 49：crossover, mutation (15 minutes and 58 seconds)

• Chapter 50：General steps of genetic algorithm (3 minutes and 48 seconds)

• Chapter 51：Characteristics of genetic algorithms (3 minutes and 24 seconds)

• Chapter 52：Basic idea of ​​ant colony algorithm (8 minutes and 14 seconds)

• Chapter 53：Basic ant colony algorithm model (19 minutes and 30 seconds)

• Chapter 54：Ant colony algorithm parameter selection (4 minutes and 39 seconds)

• Chapter 55：Application of Ant Colony Algorithm (6 minutes and 17 seconds)

• Chapter 56：Basic concepts of expert systems (7 minutes and 36 seconds)

• Chapter 57：Characteristics of expert systems (6 minutes and 53 seconds)

• Chapter 58：How expert systems work (5 minutes and 52 seconds)

• Chapter 59：Main processes and modes of knowledge acquisition (5 minutes and 20 seconds)

• Chapter 60：Establishment of expert system (12 minutes and 10 seconds)

• Chapter 61：Examples of expert systems (13 minutes and 55 seconds)

• Chapter 62：Expert system development tools (9 minutes and 41 seconds)

• Chapter 63：machine learning (4 minutes and 0 seconds)

• Chapter 64：Basic components of a learning system (10 minutes and 53 seconds)

• Chapter 65：Classification of machine learning (18 minutes and 14 seconds)

• Chapter 66：Artificial neural networks (6 minutes and 19 seconds)

• Chapter 67：Mathematical model of neurons (10 minutes and 7 seconds)

• Chapter 68：The structure and working mode of neural network (12 minutes and 35 seconds)

• Chapter 69：The structure of BP neural network (4 minutes and 45 seconds)

• Chapter 70：Two problems with BP learning algorithm (5 minutes and 52 seconds)

• Chapter 71：Basic idea of ​​BP learning algorithm (10 minutes and 42 seconds)

• Chapter 72：BP learning algorithm learning algorithm (13 minutes and 12 seconds)

• Chapter 73：Implementation of BP learning algorithm (8 minutes and 17 seconds)

• Chapter 74：Application of BP neural network in pattern recognition (13 minutes and 32 seconds)

• Chapter 75：Discrete Hopfield neural network model (7 minutes and 34 seconds)

• Chapter 76：The working method and working process of discrete Hopfield neural network (9 minutes and 12 seconds)

• Chapter 77：Stability of discrete Hopfield neural network network (6 minutes and 54 seconds)

• Chapter 78：Continuous Hopfield Neural Network (11 minutes and 45 seconds)

• Chapter 79：Application of Hopfield Neural Network in Associative Memory (13 minutes and 52 seconds)

• Chapter 80：Hopfield neural network optimization method (22 minutes and 40 seconds)