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

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:Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).https://www.coursera.org/learn/machine-learning/supplement/NKVJ0/supervised-learning

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:

Clustering: google new, sales segments

Non-clustering: The “Cocktail Party Algorithm”

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

Model Representation

image

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.

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

Cost Function – Intuition I

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

Cost Function – Intuition II

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

image

는 cost를 구하는 cost function 


https://www.coursera.org/learn/machine-learning/supplement/2GnUg/gradient-descent

Gradient Descent

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.

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


https://www.coursera.org/learn/machine-learning/supplement/QKEdR/gradient-descent-intuition

Gradient Descent Intuition

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

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https://www.coursera.org/learn/machine-learning/supplement/U90DX/gradient-descent-for-linear-regression

Gradient Descent For Linear Regression

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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

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https://www.coursera.org/learn/machine-learning/supplement/FenyC/addition-and-scalar-multiplication

Addition and Scalar Multiplication

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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

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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

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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

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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

image

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

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:Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).https://www.coursera.org/learn/machine-learning/supplement/NKVJ0/supervised-learning

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:

Clustering: google new, sales segments

Non-clustering: The “Cocktail Party Algorithm”

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

Model Representation

image

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.

image
image

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

Cost Function – Intuition I

image
image
image

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

Cost Function – Intuition II

image
image
image
image

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

image

는 cost를 구하는 cost function 


https://www.coursera.org/learn/machine-learning/supplement/2GnUg/gradient-descent

Gradient Descent

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.

image
image
image

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


https://www.coursera.org/learn/machine-learning/supplement/QKEdR/gradient-descent-intuition

Gradient Descent Intuition

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

image
image
image


https://www.coursera.org/learn/machine-learning/supplement/U90DX/gradient-descent-for-linear-regression

Gradient Descent For Linear Regression

image
image

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

image
image


https://www.coursera.org/learn/machine-learning/supplement/FenyC/addition-and-scalar-multiplication

Addition and Scalar Multiplication

image


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

image


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

image


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

image


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

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OpenID versus OAuth from the user’s perspective – cakebaker

OpenID versus OAuth from the user’s perspective – cakebaker