/cpsc330-2023W2

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UBC CPSC 330: Applied Machine Learning (2023W2)

Introduction

This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. You are looking at the version for 2023W2 (Jan-Apr 2024). Some previous offerings:

  • 2020w1 by Mike Gelbart
  • 2023s by Mehrdad Oveisi
  • 2023w1 by Varada Kolhatkar and Andrew Roth
  • 2022w2 by Giulia Toti, Mathias Lecuyer, and Amir Abdi

Instructors:

Mathias Lecuyer (201), Mehrdad Oveisi (202).

Class Schedule

Section Days Lecture Location
201 Tue, Thu 09:30 - 10:50 LSK 200
202 Tue, Thu 15:30 - 16:50 MCML 360

Course Coordinator

Mehrdad Oveisi:

  • cpsc330-admin@cs.ubc.ca
  • Please email Mehrdad Oveisi at the above email address for all administrative concerns such as CFA accommodations, extensions or exemptions due to sickness or extenuating circumstances.

Important links

Deliverable due dates (tentative)

Assessment Due date Where to find? Where to submit?
Syllabus quiz Jan 15, 11:59pm PrairieLearn (PL) PL
hw1 Jan 15, 11:59pm Github repo PL
hw2 Jan 22, 11:59pm Github repo PL
hw3 Feb 5, 11:59pm Github repo PL
hw4 Feb 12, 11:59pm Github repo PL
hw5 Feb 26, 11:59pm Github repo PL
Midterm TBD, week of Feb 26-Mar 1 TBD TBD
hw6 Mar 11, 11:59pm Github repo PL
hw7 Mar 25, 11:59pm Github repo PL
hw8 April 8, 11:59pm Github repo PL
Final exam TBD TBD TBD

Lecture schedule (tentative)

Lectures:

  • Watch the "Pre-watch" videos before each lecture.
  • You will find lecture notes from each instructor in this repository. Lectures will be posted as they become available.

Lectures:

  • The lectures will be in-person (see Class Schedule above for more details).
  • All lecture files are subject to change without notice up until they are covered in class.
  • You are expected to watch the "Pre-watch" videos before each lecture.
  • You are expected to attend the lectures.
  • You will find the lecture notes under the lectures in this repository. Lectures will be posted as they become available.
Date Topic Videos vs. CPSC 340
Jan 9 Course intro 📹
  • Pre-watch: None
  • Recap video (after lecture): 1.0
    		• n/a
    Part I: ML fundamentals and preprocessing
    Jan 11 Decision trees 📹
  • Pre-watch: 2.1, 2.2
  • After lecture: 2.3, 2.4
    		• less depth
    Jan 16 ML fundamentals 📹
  • Pre-watch: 3.1, 3.2
  • After lecture: 3.3, 3.4
    		• similar
    Jan 18 Snow day: no lecture
    Jan 23 $k$-NNs and SVM with RBF kernel 📹
  • Pre-watch: 4.1, 4.2
  • After lecture: 4.3, 4.4
    		• less depth
    Jan 25 Preprocessing, sklearn pipelines 📹
  • Pre-watch: 5.1, 5.2
  • After lecture: 5.3, 5.4
    		• more depth
    Jan 30 More preprocessing, sklearn ColumnTransformer, text features 📹
  • Pre-watch: 6.1, 6.2
    		• more depth
    Feb 1 Linear models 📹
  • Pre-watch: 7.1, 7.2, 7.3
    		• less depth
    Feb 6 Hyperparameter optimization, overfitting the validation set 📹
  • Pre-watch: 8.1,8.2
    		• different
    Feb 8 Evaluation metrics for classification 📹
  • Pre-watch: 9.2,9.3,9.4
    		• more depth
    Feb 13 Regression metrics 📹
  • Pre-watch: 10.1
    		• more depth on metrics less depth on regression
    Feb 15 Ensembles 📹
  • Pre-watch: 11.1,11.2
    		• similar
    Feb 19-23 Midterm Break (no classes)
    Feb 27 Midterm review
    Feb 28 Midterm This week, data TBD. More details will be posted on Piazza
    Feb 29 No lecture (midterm catch-up)
    Mar 5 Feature importances, model interpretation 📹
  • Pre-watch: 12.1,12.2
    		• feature importances is new, feature engineering is new
    Mar 7 Feature engineering and feature selection None less depth
    Part II: Unsupervised learning, transfer learning, different learning settings
    Mar 12 Clustering 📹
  • Pre-watch: 14.1,14.2,14.3
    		• less depth
    Mar 14 More clustering
  • Post-lecture: 15.1, 15.2, 15.3, 201 lecture recording
    		• less depth
    Mar 19 Simple recommender systems None less depth
    Mar 21 Text data, embeddings, topic modeling 📹
  • Pre-watch: 16.1,16.2
    		• new
    Mar 26 Neural networks and computer vision less depth
    Mar 28 Time series data (Optional) Humour: The Problem with Time & Timezones new
    Apr 2 Survival analysis 📹 (Optional but highly recommended)Calling Bullshit 4.1: Right Censoring new
    Part III: Communication, ethics, deployment
    Apr 4 Ethics 📹 (Optional but highly recommended)
  • Calling BS videos Chapter 5 (6 short videos, 50 min total)
  • The ethics of data science
    		•  new
    Apr 9 Communication 📹 (Optional but highly recommended)
  • Calling BS videos Chapter 6 (6 short videos, 47 min total)
  • Can you read graphs? Because I can't. by Sabrina (7 min)
    		•  new
    Apr 11 Model deployment new