Machine Learning 정리
1 Introduction
1.1 Introduction.md
1.2 Linear regression with one variable.md
1.3 Linear algebra review.md
2 Linear regression with multiple variables.md
2.1 Multivariate linear regression.md
2.2 Computing parameters analytically.md
3 Logistic regression regularization.md
3.1 Logistic regression.md
3.2 Regularization and overfitting problem.md
4 Neural networks.md
5 Neural networks learning.md
6 Advice for applying machine learning.md
7 Machine learning system design.md
8 Support vector machines.md
9 Unsupervised learning dimensionality reduction.md
10 Dimensionality reduction.md
11 Anormaly detection.md
12 Recommander systems.md
13 Large scale machine learning.md
14 Application example photo ocr.md
15 Vectorized implementations in octave.md
Exercises
ex2.1 Logistic regression
ex2.2 Regularized logistic regression
ex3 1 Multi class classification
ex3 2 Neural networks
ex4 1 Neural networks
ex4 2 Backpropagation
ex5 Regularized Linear Regression and Bias-Variance
ex6 1 Support vector machines
ex6 2 Spam classification
ex7 K-means clustering and pca
ex8 Recommander system
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3 Logistic regression regularization.md
3. Logistic Regression, Regularization
3.1 Logistic regression
3.2 Regularization and overfitting problem
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