Lecture 3: Linear Regression (with One Variable)

Supervised Learning: Regression Right answers are given for inputs Regression refers to predicting continuous valued output (e.g. price)

Notations

Learning Process

Hypothesis Representation

Hypothesis Representation

Finding Parameters

Cost Function

Cost Function Intuition

Cost Function Intuition (Cont)

Cost Function Intuition (Cont)

Cost Function Intuition (Cont)

Cost Function Intuition (Cont)

Cost Function Intuition (Cont) cc cc

Cost Function Intuition (Cont) cc cc cc

Cost Function Intuition (Cont) cc cc cc cc cc cc cc

Cost Function Intuition (Cont) cc cc cc cc cc cc cc = 1

Cost Function Intuition 2

Cost Function Intuition 2

Cost Function Intuition 2 (Cont)

Cost Function Intuition 2 (Cont)

Cost Function Intuition 2 (Cont)

Cost Function Intuition 2 (Cont)