Applied Machine Learning Notes

Supervised Learning

• Classification (Discrete Labels)
• Regression (Output is continuous, Example - Age, Stock prices)
• Past data + Past Outputs used

Unsupervised Learning

• Dimensionality reduction (Data in higher dimensions, Remove dimension without losing lot of information)
• Reducing dimensionality makes it easy for computation (Continuous values)
• Clustering (Discrete labels)
• No Past outputs, Only current data

Reinforcement Learning

• All Game Playing is unsupervised
• Learning Policy
• Negative / Positive reward for each step

Type of Models

• Inductive (Learn model, Learn from a function) vs Transductive (Lazy learning ex- Opinion from like minded people)
• Online (Learn from every new incoming tweet) vs Offline (Look past 1 Yeat tweet)
• Generative (Apply Gaussian on Data, Use ML and compute Mean / Variance) vs Discriminative (Two sides of Line)
• Parametric vs Non-Parametric Models

Happy Learning!!!