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Linear Regression, Machine learning term

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S.Rulez 1st seminar

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S.Rulez Study 1 Machine Learning / Linear Regression
Machine Learning • Limitations of explicit programming • Spam filter: many rules • Automatic driving: too many rules • Machine learning "Field of study that gives computers the ability to learn without being explicitly programmed” Arthur Samuel (1959)
Supervised/Unsupervised learning • Supervised learning • Teach the computer how to do something, then let it use it;s new found knowledge to do it • Unsupervised learning • Let the computer learn how to do something, and use this to determine structure and patterns in data
Supervised learning • Given the “right answer” for each example in the data. • learning with labeled examples - training set
Unsupervised learning • In unsupervised learning, we get unlabeled data • Just told - here is a data set, can you structure it • To cluster data into to groups - clustering algorithm • Example of clustering algorithm • Google news • Genomics • Organize computer clusters • Social network analysis - Customer data
Types of supervised learning • Predicting final exam score based on time spent • regression • Pass/non-pass based on time spent • binary classification • Letter grade (A, B, C, E and F) based on time spent • multi-label classification
Regression • a statistical process for estimating the relationships among variables. • 관찰된 연속형 변수들에 대해 두 변수 사이의 모형을 구한 뒤 적합도를 측 정해 내는 분석 방법
Linear Regression • 종속 변수 y와 한 개 이상의 독립 변수 X와의 선형 상관 관계를 모델링 • 독립 변수는 입력값이나 원인, 종속 변수는 결과물이나 효과를 뜻함
Regression (presentation)
(Linear) Hypothesis
Hypothesis • It is like your prediction machine, throw in an x value, get a putative y value
Cost function • How fit the line to our (training) data • 기대치와 실제 값의 차이를 구하면 되나, 계산의 편의를 위해 차이의 제곱을 구함 • 각 값의 차이의 제곱의 합을 나누어 평균을 구한다.
Goal: Minimize cost
Hypothesis and Cost
What cost(W) looks like?
How to minimize cost?
Gradient descent algorithm • Minimize cost function • Gradient descent is used many minimization problems • For a given cost function, cost (W, b), it will find W, b to minimize cost • It can be applied to more general function: cost (w1, w2, …)
Formal definition
Formal definition Gradient descent algorithm

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Linear Regression, Machine learning term

  • 1. S.Rulez Study 1 Machine Learning / Linear Regression
  • 2. Machine Learning • Limitations of explicit programming • Spam filter: many rules • Automatic driving: too many rules • Machine learning "Field of study that gives computers the ability to learn without being explicitly programmed” Arthur Samuel (1959)
  • 3. Supervised/Unsupervised learning • Supervised learning • Teach the computer how to do something, then let it use it;s new found knowledge to do it • Unsupervised learning • Let the computer learn how to do something, and use this to determine structure and patterns in data
  • 4. Supervised learning • Given the “right answer” for each example in the data. • learning with labeled examples - training set
  • 5. Unsupervised learning • In unsupervised learning, we get unlabeled data • Just told - here is a data set, can you structure it • To cluster data into to groups - clustering algorithm • Example of clustering algorithm • Google news • Genomics • Organize computer clusters • Social network analysis - Customer data
  • 6. Types of supervised learning • Predicting final exam score based on time spent • regression • Pass/non-pass based on time spent • binary classification • Letter grade (A, B, C, E and F) based on time spent • multi-label classification
  • 7. Regression • a statistical process for estimating the relationships among variables. • 관찰된 연속형 변수들에 대해 두 변수 사이의 모형을 구한 뒤 적합도를 측 정해 내는 분석 방법
  • 8. Linear Regression • 종속 변수 y와 한 개 이상의 독립 변수 X와의 선형 상관 관계를 모델링 • 독립 변수는 입력값이나 원인, 종속 변수는 결과물이나 효과를 뜻함
  • 11. Hypothesis • It is like your prediction machine, throw in an x value, get a putative y value
  • 12. Cost function • How fit the line to our (training) data • 기대치와 실제 값의 차이를 구하면 되나, 계산의 편의를 위해 차이의 제곱을 구함 • 각 값의 차이의 제곱의 합을 나누어 평균을 구한다.
  • 17. Gradient descent algorithm • Minimize cost function • Gradient descent is used many minimization problems • For a given cost function, cost (W, b), it will find W, b to minimize cost • It can be applied to more general function: cost (w1, w2, …)