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

スライド2：Linear regression with one variable /Model representation/Cost function/Gradient Descent

スライド3:Linear Algebra review (optional)

スライド4:Linear Regression with multiple variables/Multiple features/Feature Scaling/Features and polynomial regression/normal equation

スライド5:Octave Tutorial

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

スライド7：Regularization/The problem ofoverfitting/Cost function/Regularized linear regression / Regularized logistic regression

スライド8:Neural Networks:Representation/Non-linear hypotheses/Neurons and the brain/Model
representation/Examples and intuitions/Multi-class classification

スライド9:Neural Networks:Learning/Cost function/Backpropagation algorithm/Implementation note:Unrolling parameters/Gradient checking/Random initialization/Puttng it together

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

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

スライド12：Support　Vector　Machines/Optimization objective/Large Margin Intuition/The　mathematics behind large margin classification(optional)/Using an SVM

スライド13:Clustering/Unsupervised learning introduction/K-means algorithm/Optimization objective/Random initialization/Choosing the number of clusters

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

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

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

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

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

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

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。