CPSC 330 Section 2
Home
Lectures
Lecture 3: ML fundamentals
Overview
Lecture 1: Course introduction
Lecture 2: Terminology, baselines, decision Trees
Lecture 3: ML fundamentals
Lecture 4:
\(k\)
-nearest neighbours and SVM RBFs
Lecture 5: Preprocessing and sklearn pipelines
Lecture 6:
sklearn
column transformer and text fearutres
Lecture 3: ML fundamentals
Supervised Machine Learning Fundamentals
Slides
View slides in full screen
Outline
Generalization, data splitting
Cross-validation
Overfitting, underfitting, the fundamental tradeoff
The golden rule
Lecture 2: Terminology, baselines, decision Trees
Lecture 4:
\(k\)
-nearest neighbours and SVM RBFs