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Overview to Machine Learning


Overview to Machine Learning


A computer program is said to learn from experience E with respect to some task T and some performance measure P,if its performance on T,as measured by P,improves with experience E. - Tom Mitchell,1997

So the essential of machine learning is improving P on T by learning from E,not just E.

Premise to apply Machine Learning

  1. To complex for traditional algorithm.
  2. Adjust fluctuating environments.
  3. Getting insights about complex problems and large amounts of data.

Tips:No silver bullet.Machine Learning is not always best method to any problem.

Categories of Machine Learning

  1. supervised,unsupervised,semisupervised and reinforcement learning.
  2. incremental versus batch learning.
  3. instance-based versus model-based learning.

Supervised/Unsupervised Learning

Supervised Learning

In supervised learning,the training data you feed to the algorithm includes the desired output.It learn the map of I/O. The typical tasks applied supervised Learning:classification,regression. The typical supervised learning algorithm: - KNN(k-Nearest Neighbors) - Linear Regression - Logistic Regression - SVM(Support Vector Machine) - Decision Trees and Random Forests - NN(Neural Network)

Unsupervised Learning

In unsupervised learning,the training data you feed to the algorithm not include the desired output.It learn the inner structure of input data. The typical unsupervised learning algorithm: - Clustering - k-Means - HCA(Hierarchical Cluster Analysis) - Expectation Maximization - Visualization and dimensionality reduction - PCA(Principal Component Analysis) - Kernel PCA - LLE(Locally-Linear Embedding) - t-SNE(t-distributed Stochastic Neighbor Embedding) - Association rule learning - Apriori - Eclat

Semisupervised Learning

The learning combine supervised & unsupervised method.

Reinforcement Learning

The agent in this context,can observe the environment,select and perform actions,and get rewards in return.Agent learn policy to get maximum rewards over time.

Batch and Incremental Learning

Whether or not can learn incrementally from a stream of incoming data.

Batch Learning

Batch Learning:incapable of learning incrementally,it must be trained using all the available data. Offline Learning:first the system is trained,and then it is launched into production and runs without learning anymore.

Incremental Learning

Incremental learning: you train the system incrementally by feeding it data instances sequentially, either individually or by small groups called mini-batch. Learning Rate:how fast the system adapt to changing data. Challenge: bad data is fed to the system.

Instance-Based versus Model-Based Learning

How to generalize.

Instance-Based Learning

Instance-Based Learning: the system learns the examples by heart, then generalizes to new instance using a similarity measure.

Model-Based Learning

Model-Based Learning: build a model learning from examples to make predictions.