Coursera Machine Learning スライドまとめ

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/Modelrepresentation/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…


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