课程简介
This course will introduce the basic concepts, history, classic algorithms, and application fields of Artificial Intelligence. And in this course students will learn what Artificial Intelligence (AI) is, understand AI concepts and terms like data processing, machine learning, deep learning and neural networks. Students will also explore use cases and applications of AI such as classification, computer vision, and natural language processing.
The objectives of this course are to help students understand AI, induce their interests in learning AI, and provide new thinking methods and problem-solving methods.
课程大纲
Chapter 1 Overview of Artificial Intelligence
1.1 What is artificial intelligence
1.2 The History of artificial intelligence
1.3 Applications of Artificial Intelligence
1.4 Machine Learning and Deep Learning
Chapter 2 Machine Learning
2.1 What is Machine Learning
2.2 Supervised Learning
2.3 Unsupervised Learning
2.4 Construction of Development Environment
Chapter 3 Regression
3.1 What is Regression
3.2 Linear Regression
3.3 Logistic Regression
3.4 Practical Case
Chapter 4 Classification
4.1 What is Classification
4.2 Naive Bayes
4.3 Support Vector Machine
4.4 Practical Case
Chapter 5 Deep Learning
5.1 Introduction to Deep Learning
5.2 What is Neural Network
5.3 Neural Network Basics
5.4 Practical Case
Chapter 6 Convolutional Neural Network
6.1 What is Convolutional Neural Network
6.2 Convolution Layer
6.3 Pooling Layer
6.4 Classical Convolutional Neural Network
Chapter 7 Computer Vision
7.1 What is Computer Vision
7.2 Image Classification
7.3 Introduction to TensorFlow
7.4 Practical Case
Chapter 8 Natural Language Processing
8.1 What is Natural Language Processing
8.2 Basics of NLP
8.3 Recurrent Neural Network
8.4 Practical Case
Final Exam
1.1 What is artificial intelligence
1.2 The History of artificial intelligence
1.3 Applications of Artificial Intelligence
1.4 Machine Learning and Deep Learning
Chapter 2 Machine Learning
2.1 What is Machine Learning
2.2 Supervised Learning
2.3 Unsupervised Learning
2.4 Construction of Development Environment
Chapter 3 Regression
3.1 What is Regression
3.2 Linear Regression
3.3 Logistic Regression
3.4 Practical Case
Chapter 4 Classification
4.1 What is Classification
4.2 Naive Bayes
4.3 Support Vector Machine
4.4 Practical Case
Chapter 5 Deep Learning
5.1 Introduction to Deep Learning
5.2 What is Neural Network
5.3 Neural Network Basics
5.4 Practical Case
Chapter 6 Convolutional Neural Network
6.1 What is Convolutional Neural Network
6.2 Convolution Layer
6.3 Pooling Layer
6.4 Classical Convolutional Neural Network
Chapter 7 Computer Vision
7.1 What is Computer Vision
7.2 Image Classification
7.3 Introduction to TensorFlow
7.4 Practical Case
Chapter 8 Natural Language Processing
8.1 What is Natural Language Processing
8.2 Basics of NLP
8.3 Recurrent Neural Network
8.4 Practical Case
Final Exam