BLOCK 1 & 2 - INTRODUCTION
MACHINE
CLASS
LEARNING
💡 TOPICS COVERED: Supervised Learning and Unsupervised Learning
RECALL NOTES
Statistical Learning Vast set of tools for understanding da
Classified as supervised or unsupervis
Application for prediction or inferenc
What is Supervised Learning? Statistical model for predicting an ou
more inputs
Two categories: regression and classif
General form:
Y = f(x) + ϵ ; f(x) = (X
Regression
Linear regression
Least Squares Estimation
Used for predicting quantitative va
Advantages
Relatively simple
Interpretable inference
Drawbacks
May yield poor prediction
MACHINE
CLASS
LEARNING
💡 TOPICS COVERED: Supervised Learning and Unsupervised Learning
RECALL NOTES
Statistical Learning Vast set of tools for understanding da
Classified as supervised or unsupervis
Application for prediction or inferenc
What is Supervised Learning? Statistical model for predicting an ou
more inputs
Two categories: regression and classif
General form:
Y = f(x) + ϵ ; f(x) = (X
Regression
Linear regression
Least Squares Estimation
Used for predicting quantitative va
Advantages
Relatively simple
Interpretable inference
Drawbacks
May yield poor prediction
