![课堂小结
Main contents
For every single lesson, we provide a courseware, a teaching plan example and task list for
students to practice.
For every single experiment, we provide a experiment manual with more detailed
information to the experiment.
Course Description
Course
Hour Lessons Experiments
This course carries out project-based
learning around the theme of
"Automobile Price Prediction and
Grading ". It requires students to
analyze various parameters of the car,
determine data characteristics,
establish regression and classification
models, and predict car prices and
grades, preliminarily enabling
students to master the concepts and
processes of machine learning, and
be able to transfer the skills to solve
other problems.
8
1. Machine Can Learn Automobile dataset
2. A Preliminary Study of Regression Prediction Simple linear regression prediction
3. Importance of Data to the Model Importance of data to the model
4. Multiple Linear Regression Prediction Multiple linear regression prediction
5. A Preliminary Study of Classification [optional] Automobile classification dataset
6. Explore the Perceptron Classification Process
Explore the perceptron classification
process
7. Machine Learning Algorithm Applications
Regression and classification project
practice
8. Project Presentation
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01. Machine can learn_machine learning.pdf
- 2. 课堂小结 Main contents For every single lesson, we provide a courseware, a teaching plan example and task list for students to practice. For every single experiment, we provide a experiment manual with more detailed information to the experiment. Course Description Course Hour Lessons Experiments This course carries out project-based learning around the theme of "Automobile Price Prediction and Grading ". It requires students to analyze various parameters of the car, determine data characteristics, establish regression and classification models, and predict car prices and grades, preliminarily enabling students to master the concepts and processes of machine learning, and be able to transfer the skills to solve other problems. 8 1. Machine Can Learn Automobile dataset 2. A Preliminary Study of Regression Prediction Simple linear regression prediction 3. Importance of Data to the Model Importance of data to the model 4. Multiple Linear Regression Prediction Multiple linear regression prediction 5. A Preliminary Study of Classification [optional] Automobile classification dataset 6. Explore the Perceptron Classification Process Explore the perceptron classification process 7. Machine Learning Algorithm Applications Regression and classification project practice 8. Project Presentation 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 3. Regression and Classification Automobile Price Prediction and Grading 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 4. 01 02 04 05 03 目录 CONTENTS Learning Objectives What is Machine Learning Types of Machine Learning Tasks of Supervised Learning How does Machine Learning work Contents 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 5. 学习目标 • Knowing that machine learning is to find patterns from data • Knowing the procedure of machine learning • Understanding the characteristics of supervised learning, unsupervised learning and reinforcement learning in machine learning • Understanding the characteristics of classification and regression tasks, and be able to distinguish the types of prediction tasks in real life Learning Objectives 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 6. 01 02 04 05 03 目录 CONTENTS Learning Objectives What is Machine Learning Types of Machine Learning Tasks of Supervised Learning How does Machine Learning work Contents 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 7. 思考活动 How to teach children to recognize animals? Think Discuss Cat Dog ? 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 8. 思考活动 How to teach children to recognize animals? Think Discuss Most parents buy animal picture books for their children, and the children view various pictures of certain animals. This is a cat When the children view an animal picture that they never seen, they can recognize it based on what they have learned from the animal picture books. Cat Dog … … Dog Dog Cat Cat 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 9. 思考活动 Does it work if we teach a machine like this? Think Discuss This is a cat Cat Dog … … Dog Dog Cat Cat 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 10. 思考活动 Does it work if we teach a machine like this? Think Discuss This is a cat This is how image recognition works. Machine can learn. Cat Dog … … Dog Dog Cat Cat 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 11. What is machine learning? There is no commonly accepted and accurate definition. • Machine learning is a multi-disciplinary science that involves probability theory, statistics, approximation theory, convex analysis, algorithm complexity and other disciplines. • Machine learning is a part of artificial intelligence, and is the study of computer algorithms that can be automatically improved through experience and by the use of data Machine Learning 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 12. We are using machine learning in our daily life. Below are some most trending real-world applications of Machine Learning Face recognition Speech recognition Product recommendation • The face recognition can learn to recognize the person and identify person in the picture. • The Speech recognition is a process of converting voice instructions into text, machine learning algorithms are widely used by various applications of speech recognition • The shopping website understands the user interest using various machine learning algorithms and suggests the product as per customer interest. 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 13. 01 02 04 05 03 目录 CONTENTS Learning Objectives What is Machine Learning Types of Machine Learning Tasks of Supervised Learning How does Machine Learning work Contents 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 14. Take the dog/cat image recognition as an example 1-Collect and process data 2-Train models 3-Test models 4-Apply models Dog Dog Dog Cat Cat Cat Collect dog and cat images, and label them manually Choose a proper machine learning model based on the task and use the data to train it Collect new dog and cat labeled images to evaluate the model Apply the model to predict a new image 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 15. 01 02 04 05 03 目录 CONTENTS Learning Objectives What is Machine Learning Types of Machine Learning Tasks of Supervised Learning How does Machine Learning work Contents 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 16. In order to deal with different problems, scientists have proposed a large number of machine learning algorithms, which are classified into three types Machine Learning Supervised learning Unsupervised learning Reinforcement learning 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 17. Supervised learning Sample labeled data is provided to the machine learning system for supervised learning. The goal of supervised learning is to map sample data with the label data. Supervised learning is the most common type of machine learning algorithms. Cat Dog … … Dog Dog Cat Cat Cat 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 18. Supervised learning Supervised learning can be grouped further in two categories of algorithms: Regression (the label data are Continuous variables) and Classification (the label data are Discrete variables) Supervised learning Regression model Classification model 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 19. 思考活动 To solve the following supervised learning problem, would we use regression or classification? Think Discuss Go straight Turn left Turn right Go straight and turn left Go straight and turn right Turn left and right Recognize traffic signs $500,000 $300,000 $700,000 Predict the house price 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 20. Unsupervised learning Sample data that has not been labeled is provided to the machine learning system for unsupervised learning. The goal of supervised learning is to restructure the input data into new types or a group of objects with similar patterns. … … group1 … group2 … Image clustering in photo album 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 21. Reinforcement learning Reinforcement learning enables an agent to learn in an interactive environment by gets a reward for each right action and gets a penalty for each wrong action. The goal of an agent is to get the most reward points, and hence, it improves its performance. A chess game 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ
- 22. 课堂小结 • The basic process of machine learning: train, test and apply • Machine learning is to learn from data: supervised learning, unsupervised learning and reinforcement learning • Two categories of supervised learning: regression and classification Summary 商 汤 科 技 Ｓ Ｅ Ｎ Ｓ Ｅ Ｔ Ｉ Ｍ Ｅ