*Summary from the *Stanford's Machine learning class by Andrew Ng

*Part 1**Supervised vs. Unsupervised learning,**Linear Regression,**Logistic Regression,**Gradient Descent*

*Part 2**Regularization,**Neural Networks*

*Part 3**Debugging and Diagnostic,**Machine Learning System Design*

*Part 4**Support Vector Machine,**Kernels*

*Part 5**K-means algorithm,**Principal Component Analysis (PCA) algorithm*

*Part 6**Anomaly detection,**Multivariate Gaussian distribution*

*Part 7**Recommender Systems,**Collaborative filtering algorithm,**Mean normalization*

*Part 8**Stochastic gradient descent,**Mini batch gradient descent,**Map-reduce and data parallelism*

__Anomaly detection__

__Examples__- Fraud Detection
- x(i) = features of users i activities.
- Model p(x) from data
- Identify unusual users by checking which have p(x) < epsilon

- Manufacturing
- Monitoring computers in a data center

- Fraud Detection
__Algorithm__- Choose features x(i) that you think might be indicative of anomalous examples.
- Fit parameters u1,…un, sigma square 1,… sigma square n
- Given new example x, compute p(x):
- Anomaly if p(x) < epsilon

__Aircraft engines example__- 10000 good (normal) engines
- 20 flawed engines (anomalous)
__Alternative 1__- Training set: 6000 good engines
- CV: 2000 good engines (y=0), 10 anomalous (y=1)
- Test: 2000 good engines (y=0), 10 anomalous (y=1)

**Alternative 2**:- Training set: 6000 good engines
- CV: 4000 good engines (y=0), 10 anomalous (y=1)
- Test: 4000 good engines (y=0), 10 anomalous (y=1)

- Algorithm Evaluation

__Anomaly detection vs. Supervised learning__- Very small number of positive examples
- Large number of positive and negative examples.
- Large number of negative examples
- Enough positive examples for algorithm to get a sense of what positive examples are like, future positive examples likely to be similar to ones in training set.
- Many different “types” of anomalies. Hard for any algorithm to learn from positive examples what the anomalies look like; future anomalies may look nothing like any of the anomalous examples we’ve seen so far.
- Fraud detection
- Email spam classification
- Manufacturing (e.g. aircraft engines)
- Weather prediction (sunny/rainy/etc).
- Monitoring machines in a data center
- Cancer classification

__Anomaly detection____Supervised learning____Choose what features to use__- Plot a histogram and see the data

- Original vs. Multivariate Gaussian model