A few days ago, I mentioned that the Stanford Machine Learning class will be starting soon. I thought I should quickly mention some of the topics covered. The list also serves as a great outline for machine learning.
In supervised learning, one has a set of data with features and labels.
- Linear Regression – one/multiple variables
- Gradient Descent – a general algorithm for minimizing a function
- Logistic Regression – This is useful when predicting classification type results. For example, are you looking for a yes or no result. Does the patient have cancer? Will the customer buy my new product? It can also be helpful for more than 2 results. What color will a person choose (red, blue, green, silver)?
- Neural Networks – A learning algorithm that is modeled after the brain. Think of neurons.
- Support Vector Machines
In unsupervised learning, one has a set of data with no features and labels. Can some structure be found for the data?
- Clustering – The most popular technique is K-means.
- PCA (Principal Components Analysis) – speed up a learning algorithm
This section covers methods to determine if data is bad. Bad data is considered an anomaly.
Like the name says, recommender systems are used to make recommendations. Companies like Netflix use recommender systems to recommend new movies to customers. LinkedIn also recommends people to connect with. This is a fairly hot topic in the tech world right now.
- Content Based(Features)
- Modified Linear Regression
- Non-content Based(No Features)
- Collaborative Filtering
- Matrix Factorization
If any of these topics sound interesting to you, signup for the Stanford Machine Learning class. Professor Andrew Ng will do an excellent job explaining the details.
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