Areas of machine learning

Machine learning can be categorized into three areas according to the type of learning:

• Supervised
• Unsupervised
• Reinforcement Learning

This video covers these areas.

In supervised learning, the dataset contains the input data (X), which is also called the features, and the output label (Y), and this data is called labeled dataset. We want to train the model on the given dataset (Xi, Yi), then use the trained model to predict a new value of y when giving the model the corresponding x.

• Goal: learn a function that maps input value (x) to output value (y)
• Examples of supervised learning are classification and regression.

The video also introduces.

The video introduces unsupervised learning. In unsupervised learning, we only get raw data (only X) and we don’t have access to the ground truth label (Y). The model is trained with no supervision or guidance.

• Goal: process the data and understands patterns and discovers the outputs
• Examples: clustering, feature extraction

Discussion:

• Discuss and differentiate between supervised and unsupervised learning and lists some of the popular algorithms for classification and clustering.