机器学习练习 - ML_Coursera

实验1：线性回归 (Linear Regression)

1. 单变量的线性回归 (Linear regression with one variable)
   • "To predict prots for a food truck"
   • Plotting Data
   • Gradient Descent
   • Visualizing J
2. 多变量的线性回归 (Linear regression with multiple variables)
   • "To predict the prices of houses"
   • Feature Normalization
   • Gradient Descent
   • Normal Equations

实验2：逻辑回归(Logistic Regression)

1. 两变量逻辑回归 (Logistic Regression)
   • "To predict whether a student gets admitted into a university"
   • Visualizing the data
   • Compute Cost and Gradient
   • Optimizing using fminunc
   • Predict and Accuracies
2. 正则化的逻辑回归 (Regularized logistic regression)
   • "To predict whether microchips from a fabrication plant passes QA"
   • Visualizing the data
   • Feature mapping
   • Regularization and Accuracies

实验3：多元分类与神经网络(Multi-class Classification & Neural Networks)

1. 多元分类 (Multi-class Classication)
   • "To recognize handwritten digits (from 0 to 9)"
   • Loading and Visualizing Data
   • Vectorize Logistic Regression
   • One-Vs-All
2. 神经网络 (Neural Networks)
   • "To recognize handwritten digits"
   • 向前传播 (forward propagate)

实验4：神经网络学习( Neural Network Learning)

1. 神经网络学习 (Neural Network Learning)
   • "To the task of hand-written digit recognition"
   • Loading and Visualizing Data
   • Compute Cost (Feedforward) *
   • Sigmoid Gradient
   • Implement Backpropagation *
   • Implement Regularization
   • Training NN
   • Visualize Weights *
   • Implement Predict
   • Gradient Checking

实验5：Regularized Linear Regression & Bias/Variance

1. Regularized Linear Regression & Bias/Variance (Polynomial Regression)
   • "To predict the amount of water flowing out of a dam using the changeof water level in a reservoir"
   • "Go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance."
   • Loading and Visualizing Data
   • Train Linear Regression
   • Learning Curve for Linear Regression
   • Feature Mapping for Polynomial Regression(Normalize)
   • Learning Curve for Polynomial Regression (Different Lambda)
   • Validation for Selecting Lambda

实验6：Support Vector Machines (SVM)

1. Support Vector Machines (SVMs)
   • "using support vector machines(SVMs) with various example 2D datasets"
   • try different values of C on this dataset
   • Gaussian Kernel
   • determined the best C and $ parameters to use
2. Spam Classification with SVMs
   • "using SVMs to build your own spam filter"
   • Preprocessing Emails
   • Vocabulary List
   • Training SVM for Spam Classification
   • Test Spam Classification
   • Top Predictors of Spam
   • Try Your Own Emails

实验7：K-means Clustering & Principal Component

1. K-means Clustering
   • "implement the K-means algorithm & use it for image compression"
   • Find Closest Centroids
   • Compute Means
   • K-Means Clustering
   • Random initialization
   • K-Means Clustering on Pixels
   • Use your own image (different K)
2. Principal Component Analysis (PCA)
   • "use PCA to perform dimensionality reduction(Face Image Dataset)"
   • implement PCA
   • Dimension Reduction:Projecting the data onto the principal components
   • Reconstructing an approximation of the data
   • Dimension Reduction for Faces

实验8：Anomaly Detection and Recommender Systems

1. Anomaly detection
   • "detect anomalous behavior in server computers"
   • Estimating parameters for a Gaussian
   • Selecting the threshold (F1 Score)
   • High dimensional dataset
2. Recommender Systems (collaborative filtering learning algorithm)
   • "implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings"
   • Collaborative ltering cost function
   • Collaborative ltering gradient
   • Regularized