文章目录
I. Machine Learning
1. Tom Mitshell’s Definition of Machine Learning
- Task 任务
- Experience 经验
- Performance 性能(Reward Function 收益函数)
2. Course - Machine Learning: Classification
-
Case Studies: Analyzing Sentiment & Loan Default Prediction
-
In our case study on analyzing
sentiment
, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,…). In our second case study for this course,loan default prediction
, you willtackle
financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are examples ofclassification
, one of the most widely used areas of machine learning, with a broad array of applications, includingad targeting
,spam detection
,medical diagnosis
andimage classification
.
II. Human
1. What Is Human?
(1) from dictionary
- A member of the genus Homo and especially of the species H. sapiens.
- A person
(2) from Warcraft (A Game)
- Human beings define themselves in biological, social, and spiritual terms. Biologically, humans are classified as the species Homo sapiens (Latin for “knowing man”): a bipedal primate belonging to the superfamily of Hominoidea, with all of the apes: chimpanzees, gorillas, orangutans, and gibbons
(3) from Albert Einstein
- Man is, at one and the same time, a solitary being and a social being. As a solitary being, he attempts to protect his own existence and that of those who are closest to him, to satisfy his personal desires, and to develop his innate abilities. As a social being, he seeks to gain the recognition and affection of his fellow human beings, to share in their pleasures, to comfort them in their sorrows, and to improve their conditions of life.’ - Albert Einstein (in essay ‘Why Socialism’)
2. Classification
- human - humanity
- class - classify - classification
- word stress - emphysis - importance
(1) sex / gender
- boys and girls (boy and girl)
- men and women (man and woman)
- sexual appeal - sexuality
(2) age
- infants
- children
- teenagers
- young people
- middle-aged people
- old people