Coursera Machine Learning スライドまとめ

# はじめに
CourseraのMachineLearningの復習用にと探したら、スライドのまとめがありましたので、まとめました。

# スライド一覧

[スライド1：Introduction Welcome](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture1.pdf)

[スライド2：Linear regression with one variable /Model representation/Cost function/Gradient Descent](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture2.pdf)

[スライド3:Linear Algebra review (optional)](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture3.pdf)

[スライド4:Linear Regression with multiple variables/Multiple features/Feature Scaling/Features and polynomial regression/normal equation](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture4.pdf)

[スライド5:Octave Tutorial](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture5.pdf)

[スライド6:Logistic Regression/Classification/Hypothesis Representation/Decision boundary/Simplified cost function and gradient descent/Advanced optimization/Multiclass classification:One vs all](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture6.pdf)

[スライド7：Regularization/The problem ofoverfitting/Cost function/Regularized linear regression / Regularized logistic regression](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture7.pdf)

[スライド8:Neural Networks:Representation/Non-linear hypotheses/Neurons and the brain/Model
representation/Examples and intuitions/Multi-class classification](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture8.pdf)

[スライド9:Neural Networks:Learning/Cost function/Backpropagation algorithm/Implementation  note:Unrolling parameters/Gradient checking/Random initialization/Puttng it together](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture9.pdf)

[スライド10:Advice for applying machine learning/Deciding what to try next/Evaluating a hypothesis/Model　selection and training/validation/test sets/Diagnosing bias vs. variance/Regularization and bias/variance/Learning curves](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture10.pdf)

[スライド11:Machine learning system design/Prioritizing what to work on:Spam classification example/Error metrics for skewed classes/Trading off precision and recall/Data　for　machine　learning](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture11.pdf)

[スライド12：Support　Vector　Machines/Optimization objective/Large Margin Intuition/The　mathematics behind large margin classification(optional)/Using an SVM](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture12.pdf)

[スライド13:Clustering/Unsupervised learning introduction/K-means algorithm/Optimization objective/Random initialization/Choosing the number of clusters](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture13.pdf)

[スライド14:Dimensionality Reduction:Data Compression/Data Visualization/Principal Component 
Analysis problem formulation/Principal Component Analysis algorithm/Reconstruction from compressed representation/Choosing the number of principal components/Advice for applying PCA](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture14.pdf)

[スライド15:Anomaly detection/Problem  motivation/Gaussian distribution/Developing and evaluating an anomaly detection system/Anomaly detection vs. supervised learning/Choosing what features to use/Mul-variate Gaussian distribution/Anomaly detection using the multivariate Gaussian distribution](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture15.pdf)

[スライド16:Recommender Systems/Problem formulation/Content based recommendations/Collaborative filtering/Collaborative filtering algorithm/Vectorization:Low rank matrix factorization/Implementational detail:Mean normalization](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture16.pdf)

[スライド17:Large scale machine learning:Learning with large datasets/Stochastic gradient descent/Mini　batch gradient　descent/Stochastic gradient descent convergence/Online learning/Map reduce and data parallelism](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture17.pdf)

[スライド18:Application example:Photo OCR/Problem description and pipeline/Sliding windows/Getting lots of data:Artificial data synthesis/Ceiling analysis: What part of the pipeline to work on next](https://d396qusza40orc.cloudfront.net/ml/docs/slides/Lecture18.pdf)


# 終わりに

この存在をもっと早く知りたかった。
下記に記載がありました。
[Discussion Forums](https://www.coursera.org/learn/machine-learning/discussions/all/threads/3dC-9-L0EeSgrSIAC1ALYw/replies/KaH6wBHxEeWHGCIACznL2w?page=2)

# 追記(2016/10/23)：
コースの各weekから、各スライドに飛べるようになってました。