andrew-ng-old-matlab-machine-learning-course

Here are all the programming exercises from the Standford professor Andrew Ng's machine learning course.

Exercise 1

Linear regression

• Calculate cost function for linear regression
• Calculate gradient descent for linear regression

Exercise 2

Logistic regression & regularization

• Sigmoid function for logistic regression
• Cost for logistic regression
• Gradient for logistic regression
• Prediction function
• Compute cost for regularized LR
• Gradient for regularized LR

Exercise 3

Multiclass logistic regression

• Regularized Logisic Regression
• One-vs-all classifier training
• One-vs-all classifier prediction
• Neural Network Prediction Function

Exercise 4

Neural networks

• Feedforward and Cost Function
• Regularized Cost Function
• Sigmoid Gradient
• Neural Net Gradient Function (Backpropagation)
• Regularized Gradient

Exercise 5

Regularized Linear Regression & Bias X variance

• Regularized Linear Regression Cost Function
• Regularized Linear Regression Gradient
• Learning Curve
• Polynomial Feature Mapping
• Cross Validation Curve

Exercise 6

Support Vector Machines (SMV)

• Gaussian Kernel
• Parameters (C, σ) for Dataset 3
• Email Preprocessing
• Email Feature Extraction

Exercise 7

K-means Clustering & Principal Component Analysis (PCA)

• Find Closest Centroids
• Compute Centroid Means
• PCA
• Project Data
• Recover Data

Exercise 8

Anomaly Detection & Recommender Systems

• Estimate Gaussian Parameters
• Select Threshold
• Collaborative Filtering Cost
• Collaborative Filtering Gradient
• Regularized Cost
• Gradient with regularization