Summary of CMPT-3510-Machine Learning I-A01,H02-XL-2025 Wtr

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CMPT-3510-Machine Learning I-A01,H02-XL-2025 Wtr

Skill Level: Beginner

General

COURSE EVALUATION - Your opinion matters.

Instructor Announcements

Student Discussions Forum

Course Outline and Course Syllabus

CMPT 3510: Machine Learning I - Course Outline

CMPT 3510: Machine Learning I - Fall 2024 A01, H02, X01, X02 Course Syllabus

APA Citation Tutorial &amp; Resources

Introduction

Course Introduction

Assignments

Assignment: Generalization Experiment

Project: Machine Learning Proof of Concept

Project Description

Project Guidance

Project Team Selection

Notes about the project

Project Phase 1: Classification

Project Reflection

Time series prediction example

Project Phase 2: Regression

Project Final Presentation

Unit 1: Introduction to Machine Learning (ML)

Unit 1: Introduction to Machine Learning (ML)

Unit 1: Introduction to Machine Learning

Unit 1 Slides (Old)

Unit 2: The k-Nearest Neighbor (k-NN) Classifier

Unit 2: The k-Nearest Neighbor (k-NN) Classifier

Lecture Notebook for Unit 2

Whiteboard for Class of 21-01-25

Whiteboard for Class of 28-01-25

Lecture Notebook for Normalization

Normalization Lecture Video

Lecture Notebook for Normalization

Whiteboard for Class of 25-02-25 (Normalization)

Unit 2 Slides (Old)

Discussion Activity: The Implementation of the k-NN Algorithm

KNN_Multiclass

Unit 3: The Decision Tree Classifier

Unit 3: The Decision Tree Classifier

Lecture Notebook for Unit 3

Whiteboard for Class of 04-02-25

Whiteboard for Class of 11-02-25

Whiteboard for Class of 12-02-25

Unit 3 Slides (Old)

Discussion Activity: Decision Tree Weaknesses

Unit 4: Regressors

Unit 4: Regressors

Lecture Notebook for Units 4, 8 and 9

Whiteboard for Class of 26-02-25

Whiteboard for Class of 05-03-25

Whiteboard for Class of 11-03-25 Part 1

Whiteboard for Class of 11-03-25 Part 2

Whiteboard for Class of 12-03-25

Whiteboard for Class of 18-03-25

Whiteboard for Class of 19-03-25

Unit 4 Slides (Old)

Discussion Activity: What is a linear regressor?

Unit 5: Feature Engineering Basics

Unit 5: Feature Engineering Basics

Lecture Notebook for Unit 5

Whiteboard for Class of 19-03-25

Unit 5 Slides (Old)

Discussion Activity: Feature Engineering

Unit 6: Theoretical Concepts of Machine Learning

Unit 6: The Goal and Process of Machine Learning

Lecture Notebook for Unit 6

Whiteboard for Class of 25-03-25

Whiteboard for Class of 26-03-25

Unit 6 Slides (Old)

Discussion Activity: Bias and Variance Tradeoff

Unit 7: Ensemble Methods

Unit 7: Ensemble Methods

Lecture Notebook for Unit 7

Whiteboard for Class of 02-04-25

Unit 7 Slides (Old)

Discussion Activity: Random Forests

Units 8 & 9: Optimization

Units 8 &amp; 9: Optimization

Lecture Notebook for Units 4, 8 and 9

APA Citation Tutorial & Resources

Units 8 & 9: Optimization