key words: Gradient Descent,Supervised Learning ,Unsupervised Learning, Cost Function,

## Supervised Learning

In supervised learning, we are given a data set , having the idea that there is a relationship between the input and the output.

Supervised learning problems are categorized into “regression” and “classification” problems.

(a) Regression 예시- Given a picture of a person, we have to predict their age on the basis of the given picture

(b) Classification 예시 – Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

https://www.coursera.org/learn/machine-learning/lecture/olRZo/unsupervised-learning

## Unsupervised Learning

Supervised Learning, we were told explicitly what is the so-called right answer.

In Unsupervised Learning, we’re given data that doesn’t have any labels or that all has the same label or really no labels. this is called a clustering algorithm. One example where clustering is used is in Google News. Google News has done is look for tens of thousands of news stories and automatically cluster them together. So, the news stories that are all about the same topic get displayed together.

https://www.coursera.org/learn/machine-learning/supplement/1O0Bk/unsupervised-learning

## Unsupervised Learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning there is no feedback based on the prediction results.

Example:

Non-clustering: The “Cocktail Party Algorithm”

https://www.coursera.org/learn/machine-learning/supplement/cRa2m/model-representation

## Model Representation

data set 과 training set은 같은 뜻이며 처음 원천적으로 주어지는 data이다.

m 은 data set에서의 example갯수, 즉 data set row갯수이다.

x와 y 위에 지수처럼 있는 ( )는 data set에서의 index. 즉 몇번째 example인지를 알려준다.

https://www.coursera.org/learn/machine-learning/supplement/nhzyF/cost-function

## Cost Function

We can measure the accuracy of our hypothesis function by using a cost function.

https://www.coursera.org/learn/machine-learning/supplement/u3qF5/cost-function-intuition-i

## Cost Function – Intuition I

https://www.coursera.org/learn/machine-learning/supplement/9SEeJ/cost-function-intuition-ii

## Cost Function – Intuition II

는 예상기울기 예상biased 값을 이용해서 나온 예상값이다. Ɵ0 는 biased value 이고 Ɵ1는 기울기이다.

는 cost를 구하는 cost function

So we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That’s where gradient descent comes in.

:=  이 기호는 우측계산 값을 오른 쪽에 대입한다는 의미이다.  수학에서 a = b는 a와 b는 같다는 claim이다.

이전의 예시에서는 slope, biased value 두개를 고려한 예시들이었다. 여기서는 이해를 위해 slope 하나만 고려한다.

## Gradient Descent For Linear Regression

위의 설명에서 시그마안의 내용이 이해가 잘 가지 않았으나 실제로 쎄타에대해  편미분을 해보면 식이 나오는 것을 알수 있었다. 참조 사향 ) https://youtu.be/vsWrXfO3wWw?t=893

https://www.coursera.org/learn/machine-learning/supplement/Q6mSN/matrices-and-vectors

## Matrices and Vectors

https://www.coursera.org/learn/machine-learning/lecture/aQDta/matrix-vector-multiplication

https://www.coursera.org/learn/machine-learning/supplement/cgVgM/matrix-vector-multiplication

## Matrix-Vector Multiplication

https://www.coursera.org/learn/machine-learning/lecture/dpF1j/matrix-matrix-multiplication

https://www.coursera.org/learn/machine-learning/supplement/l0myT/matrix-matrix-multiplication

## Matrix-Matrix Multiplication

https://www.coursera.org/learn/machine-learning/lecture/W1LNU/matrix-multiplication-properties

https://www.coursera.org/learn/machine-learning/supplement/Xl0xT/matrix-multiplication-properties

## Matrix Multiplication Properties

https://www.coursera.org/learn/machine-learning/lecture/FuSWY/inverse-and-transpose

https://www.coursera.org/learn/machine-learning/supplement/EcNto/inverse-and-transpose

## Inverse and Transpose

key words: Gradient Descent,Supervised Learning ,Unsupervised Learning, Cost Function,

## Supervised Learning

In supervised learning, we are given a data set , having the idea that there is a relationship between the input and the output.

Supervised learning problems are categorized into “regression” and “classification” problems.

(a) Regression 예시- Given a picture of a person, we have to predict their age on the basis of the given picture

(b) Classification 예시 – Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

https://www.coursera.org/learn/machine-learning/lecture/olRZo/unsupervised-learning

## Unsupervised Learning

Supervised Learning, we were told explicitly what is the so-called right answer.

In Unsupervised Learning, we’re given data that doesn’t have any labels or that all has the same label or really no labels. this is called a clustering algorithm. One example where clustering is used is in Google News. Google News has done is look for tens of thousands of news stories and automatically cluster them together. So, the news stories that are all about the same topic get displayed together.

https://www.coursera.org/learn/machine-learning/supplement/1O0Bk/unsupervised-learning

## Unsupervised Learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning there is no feedback based on the prediction results.

Example:

Non-clustering: The “Cocktail Party Algorithm”

https://www.coursera.org/learn/machine-learning/supplement/cRa2m/model-representation

## Model Representation

data set 과 training set은 같은 뜻이며 처음 원천적으로 주어지는 data이다.

m 은 data set에서의 example갯수, 즉 data set row갯수이다.

x와 y 위에 지수처럼 있는 ( )는 data set에서의 index. 즉 몇번째 example인지를 알려준다.

https://www.coursera.org/learn/machine-learning/supplement/nhzyF/cost-function

## Cost Function

We can measure the accuracy of our hypothesis function by using a cost function.

https://www.coursera.org/learn/machine-learning/supplement/u3qF5/cost-function-intuition-i

## Cost Function – Intuition I

https://www.coursera.org/learn/machine-learning/supplement/9SEeJ/cost-function-intuition-ii

## Cost Function – Intuition II

는 예상기울기 예상biased 값을 이용해서 나온 예상값이다. Ɵ0 는 biased value 이고 Ɵ1는 기울기이다.

는 cost를 구하는 cost function

So we have our hypothesis function and we have a way of measuring how well it fits into the data. Now we need to estimate the parameters in the hypothesis function. That’s where gradient descent comes in.

:=  이 기호는 우측계산 값을 오른 쪽에 대입한다는 의미이다.  수학에서 a = b는 a와 b는 같다는 claim이다.

이전의 예시에서는 slope, biased value 두개를 고려한 예시들이었다. 여기서는 이해를 위해 slope 하나만 고려한다.

## Gradient Descent For Linear Regression

위의 설명에서 시그마안의 내용이 이해가 잘 가지 않았으나 실제로 쎄타에대해  편미분을 해보면 식이 나오는 것을 알수 있었다. 참조 사향 ) https://youtu.be/vsWrXfO3wWw?t=893

https://www.coursera.org/learn/machine-learning/supplement/Q6mSN/matrices-and-vectors

## Matrices and Vectors

https://www.coursera.org/learn/machine-learning/lecture/aQDta/matrix-vector-multiplication

https://www.coursera.org/learn/machine-learning/supplement/cgVgM/matrix-vector-multiplication

## Matrix-Vector Multiplication

https://www.coursera.org/learn/machine-learning/lecture/dpF1j/matrix-matrix-multiplication

https://www.coursera.org/learn/machine-learning/supplement/l0myT/matrix-matrix-multiplication

## Matrix-Matrix Multiplication

https://www.coursera.org/learn/machine-learning/lecture/W1LNU/matrix-multiplication-properties

https://www.coursera.org/learn/machine-learning/supplement/Xl0xT/matrix-multiplication-properties

## Matrix Multiplication Properties

https://www.coursera.org/learn/machine-learning/lecture/FuSWY/inverse-and-transpose

https://www.coursera.org/learn/machine-learning/supplement/EcNto/inverse-and-transpose

## Inverse and Transpose

OpenID versus OAuth from the user’s perspective – cakebaker

OpenID versus OAuth from the user’s perspective – cakebaker