# Machine Learning
NTNU 機器學習
##### [Back to Note Overview](https://hackmd.io/@NTNUCSIE112/NTNUNote)

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## Score
- Homework 30
- Quiz 5
- Q&A 10
- Examination 25
- Presentation 30

## Note
### Ch.01 Introduction
#### Traditional AI v.s. ML
- Traditional AI
- 手動增加 explicit rules
- ML
- 自動從大量範例中學習規則
- DL
- using parallel simple algorithms to extract rules

#### Regularity
$\theta$: parameter
- $y=f(x)$
- 連 form 不知道
- $y=f(x|\theta)$
- 表示知道 form 不知道 parameter
- 從 training data 裡面找到 parameter
- Linear Model
- $y=a_1x+a_0, \theta = (a_0, a_1)$
- Quadratic model
- $y=a_2x^2+a_1x+a_0, \theta = (a_0, a_1, a_2)$
- Gaussian model
- $y=\frac{1}{\sqrt{2\pi}\sigma}\exp\frac{(x-\mu)^2}{2\sigma^2}, \theta = (\sigma, \mu)^T$
### Ch.02 Supervised Learning
### Ch.04 Parametric Methods
### Ch.05 Multivariate Methods
### Ch.06 Demensionality Reduction
### Ch.09 Decision Trees
### Ch.11 Multilayer Perceptrons
### Ch.12 Deep Learning

### Quiz
#### 0914
- What kind of probability is often used to expressed associations among data?
- Regularities of data are often expressed as __.
- Address the difference between the expressions $f(x)$ and $f(x|q)$