Code for coursera machine learning programming assignment.
Assignment 1: Linear Regression[Week2]
| Part | Name | Score |
|---|---|---|
| 1 | Warm up exercise | 10 / 10 |
| 2 | Compute cost for one variable | 40 / 40 |
| 3 | Gradient descent for one variable | 50 / 50 |
| 4 | Feature normalization | 0 / 0 |
| 5 | Compute cost for multiple variables | 0 / 0 |
| 6 | Gradient descent for multiple variables | 0 / 0 |
| 7 | Normal equations | 0 / 0 |
Assignment 2: Logistic Regression[Week3]
| Part | Name | Score |
|---|---|---|
| 1 | Sigmoid function | 5 / 5 |
| 2 | Compute cost for logistic regression | 30 / 30 |
| 3 | Gradient for logistic regression | 30 / 30 |
| 4 | Predict function | 5 / 5 |
| 5 | Compute cost for regularized LR | 15 / 15 |
| 6 | Gradient for regularized LR | 15 / 15 |
Assignment 3: Multi-class Classification and Neural Networks[Week4]
| Part | Name | Score |
|---|---|---|
| 1 | Regularized logistic regression | 30 / 30 |
| 2 | One-vs-all classifier training | 20 / 20 |
| 3 | One-vs-all classifier prediction | 20 / 20 |
| 4 | Neural network prediction function | 30 / 30 |
Assignment 4: Neural Network Learning[Week5]
| Part | Name | Score |
|---|---|---|
| 1 | Feedforward and cost function | 30 / 30 |
| 2 | Regularized cost function | 15 / 15 |
| 3 | Sigmoid gradient | 5 / 5 |
| 4 | Neural net gradient function (backpropagation) | 40 / 40 |
| 5 | Regularized gradient | 10 / 10 |
Assignment 5: Regularized Linear Regression and Bias/Variance[Week6]
| Part | Name | Score |
|---|---|---|
| 1 | Regularized linear regression cost function | 25 / 25 |
| 2 | Regularized linear regression gradient | 25 / 25 |
| 3 | Learning curve | 20 / 20 |
| 4 | Polynomial feature mapping | 10 / 10 |
| 5 | Cross validation curve | 20 / 20 |
Assignment 6: Support Vector Machines[Week7]
| Part | Name | Score |
|---|---|---|
| 1 | Gaussian kernel | 25 / 25 |
| 2 | Parameters (C, sigma) for dataset 3 | 25 / 25 |
| 3 | Email preprocessing | 25 / 25 |
| 4 | Email feature extraction | 25 / 25 |
Assignment 7: K-Means Clustering and PCA[Week8]
| Part | Name | Score |
|---|---|---|
| 1 | Find closest centroids | 30 / 30 |
| 2 | Compute centroid means | 30 / 30 |
| 3 | PCA | 20 / 20 |
| 4 | Project data | 10 / 10 |
| 5 | Recover data | 10 / 10 |
Assignment 8: Anomaly Detection and Recommender Systems[Week9]
| Part | Name | Score |
|---|---|---|
| 1 | Estimate gaussian parameters | 15 / 15 |
| 2 | Select threshold | 15 / 15 |
| 3 | Collaborative filtering cost | 20 / 20 |
| 4 | Collaborative filtering gradient | 30 / 30 |
| 5 | Regularized cost | 10 / 10 |
| 6 | Gradient with regularization | 10 / 10 |