Skip to main content
Ctrl+K
Logo image

Things you should know

  • Syllabus
  • CPSC 330 Documents
  • Course Learning Objectives

Lectures

  • Lecture 1: Course Introduction
  • Lecture 2: Terminology, Baselines, Decision Trees
  • Lecture 3: Machine Learning Fundamentals
  • Lecture 4: \(k\)-Nearest Neighbours and SVM RBFs
  • Lecture 5: Preprocessing and sklearn pipelines
  • Lecture 6: sklearn ColumnTransformer and Text Features
  • Lecture 7: Linear Models
  • Lecture 8: Hyperparameter Optimization and Optimization Bias
  • Lecture 9: Classification metrics
  • Lecture 10: Regression metrics
  • Lecture 11: Ensembles
  • Lecture 12: Feature importances and model transparency
  • Lecture 13: Feature engineering and feature selection
  • Lecture 14: K-Means Clustering
  • Lecture 15: More Clustering
  • Lecture 16: Recommender Systems
  • Lecture 17: Introduction to natural language processing
  • Lecture 18: Multi-class classification and introduction to computer vision
  • Lecture 19: Time series
  • Lecture 20: Survival analysis
  • Lecture 21: Communication
  • Lecture 23: Deployment and conclusion
  • Final review guiding questions
  • Bonus: A high-level quick introduction to LLMs

Demos

  • Lecture 3: Class demo
  • Lecture 4: Class demo
  • Lectures 5 and 6: Class demo
  • Lectures 7: Class demo
  • Exploring classification metrics
  • Lecture 14: Class demo
  • Lecture 15: Class demo

Appendices

  • Appendix A: Common features used in text classification
  • Appendix B: K-Means customer segmentation case study
  • Appendix C: Representing documents using embeddings

Attribution

  • Attributions
  • LICENSE
  • .md

CPSC 330 Documents

CPSC 330 Documents#

  • Syllabus and administrative info

  • CPSC 330 vs. CPSC 340

  • Homework submission instructions

  • Grading policies

  • Environment setup instructions

  • Git info

  • How to ask for help

  • Python notes

  • Textbooks and online resources

previous

Syllabus

next

Course Learning Objectives

By Varada Kolhatkar

© Copyright 2022.