Exercises of Machine Learning Course taught by Prof. Andrew Ng on Coursera. --- 2013.11.09
Videos and Slides of the course: http://pan.baidu.com/s/1qWnnRaO
Programming Exercise 1: Linear Regression (Week2)
- Simple octave function
- Linear regression with one variable
- Linear regression with multiple variables
Programming Exercise 2: Logistic Regression (Week3)
- Logistic Regression
- Regularized logistic regression
--- 2013.11.09
Programming Exercise 3: Multi-class Classification and Neural Networks (Week4)
- One-vs-all Classification based on Logistic Regression
- Feedforward Propagation and Prediction
Programming Exercise 4: Neural Networks Learning (Week5)
- Feedforward and Cost Function
- Backpropagation Algorithm
- Visualizing the hidden layer
--- 2013.11.20
Programming Exercise 5: Regularized Linear Regression and Bias v.s. Variance (Week6)
- Regularized Linear Regression
- Bias-variance
- Polynomial regression
--- 2013.12.08
Programming Exercise 6: Support Vector Machines (Week7)
- Support Vector Machines
- Spam Classification
--- 2013.12.13
Programming Exercise 7: K-means Clustering and Principal Component Analysis (Week8)
- K-means Clustering
- Principal Component Analysis
--- 2013.12.19
Programming Exercise 8: Anomaly Detection and Recommender (Week9)
- Anomaly detection
- Recommender Systems
--- 2014.01.01