Schedule and Deliverables

Lecture schedule (tentative)

Live lectures: The lectures will be in-person.

This course will be run in a semi flipped classroom format. There will be pre-watch videos for many lectures, at least in the first half of the course. All the videos are available on YouTube and are posted in the schedule below. Try to watch the assigned videos before the corresponding lecture. During the lecture, we’ll summarize the important points from the videos and focus on demos, iClickers, and Q&A.

We’ll be developing lecture notes directly in this repository. So if you check them before the lecture, they might be in a draft form.

Date	Topic	Assigned videos	vs. CPSC 340
May 12	Course intro (L1)	📹 Pre-watch: 1.0	n/a
May 14	Decision trees (L2)	📹 Pre-watch: 2.1, 2.2, 2.3, 2.4	less depth
May 16	ML fundamentals (L3)	📹 Pre-watch: 3.1, 3.2, 3.3, 3.4	similar
May 16	\(k\)-NNs and SVM with RBF kernel (L4)	📹 Pre-watch: 4.1, 4.2, 4.3, 4.4	less depth
May 21	Preprocessing, sklearn pipelines (L5)	📹 Pre-watch: 5.1, 5.2, 5.3, 5.4	more depth
May 21	More preprocessing, sklearn ColumnTransformer, text features (L6)	📹 Pre-watch: 6.1, 6.2	more depth
May 23	Linear models (L7)	📹 Pre-watch: 7.1, 7.2, 7.3	less depth
May 23	Hyperparameter optimization, overfitting the validation set (L8)	📹 Pre-watch: 8.1, 8.2	different
May 26	Evaluation metrics for classification (L9)	📹 Reference: 9.2, 9.3,9.4	more depth
May 26	Regression metrics (L10)	📹 Pre-watch: 10.1	more depth on metrics less depth on regression
May 28	Midterm review
May 28-31	Midterm 1 - no tutorials during exam window
May 30	Ensembles (L11)	📹 Pre-watch: 11.1, 11.2	similar
June 2	Feature importances, model interpretation (L12)	📹 Pre-watch: 12.1,12.2	feature importances is new, feature engineering is new
June 4	Feature engineering and feature selection (L13)	None	less depth
June 6	Clustering (L14)	📹 Pre-watch: 14.1, 14.2, 14.3	less depth
June 6	More clustering (L15)	📹 Pre-watch: 15.1, 15.2, 15.3	less depth
June 9	Simple recommender systems (L16)		less depth
June 9	Text data, embeddings, topic modeling (L17)	📹 Pre-watch: 16.1, 16.2	new
June 11	Midterm review
June 11-14	Midterm 2 - no tutorials during exam window
June 13	Neural networks and computer vision (L18)		less depth
June 13	Time series data (L19)	(Optional) Humour: The Problem with Time & Timezones	new
June 16	Survival analysis (L20)	📹 (Optional but highly recommended)Calling Bullshit 4.1: Right Censoring	new
June 16	Ethics (L21)	📹 (Optional but highly recommended) Calling BS videos Chapter 5 (6 short videos, 50 min total) The ethics of data science	new
June 18	Communication (L22) and LLMs (L23)	📹 Required: first 3 modules of MODERN-DAY ORACLES or BULLSHIT MACHINES? 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

Summer Teaching Schedule (tenative)

• Mon May 12: Intro & Lecture 1

• Wed May 14: Lectures 2

• Fri May 16: Lectures 3 & 4

• Mon May 19: Holiday

• Wed May 21: Lectures 5 & 6

• Fri May 23: Lectures 7 & 8

• Mon May 26: Lectures 9 & 10

• Wed May 28: Review

  • Midterm 1 May 28-31

• Fri May 30: Lectures 11

• Mon June 2: Lectures 12

• Wed June 4: Lectures 13

• Fri June 6: Lectures 14 & 15

• Mon June 9: Lectures 16 & 17

• Wed June 11: Review

  • Midterm 2 June 11-14

• Fri June 13: Lectures 18 & 19

• Mon June 16: Lectures 20 & 21

• Wed June 18: Lectures 22 & 23

Deliverable due dates (tentative)

Usually the homework assignments will be due on Mondays and will be released on Tuesdays (the links below will lead to a “page not found” if the assignment has not been published yet).

Important: use the link in the Canvas course to access Gradescope for the first time, so that your accounts are correctly linked. Failing to do so will result in delays in getting your grades and risk of miscalculations.

Assessment	Due date	Where to find?	Where to submit?
Syllabus quiz	May 16, 22:00 pm	PrairieLearn	PrairieLearn
hw1	May 16, 22:00 pm	GitHub repo	Gradescope
hw2	May 20, 22:00 pm	GitHub repo	Gradescope
hw3	May 23, 22:00 pm	GitHub repo	Gradescope
hw4	May 26, 22:00 pm	[GitHub repo	Gradescope
Midterm 1	May 28-31	PrairieLearn (CBTF, in person)	PrairieLearn (CBTF, in person)
hw5	June 2, 22:00 pm	GitHub repo	Gradescope
hw6	June 6, 22:00 pm	GitHub repo	Gradescope
Midterm 2	June 11-14	PrairieLearn (CBTF, in person)	PrairieLearn (CBTF, in person)
hw7	June 9, 22:00 pm	GitHub repo	Gradescope
hw8	June 16, 22:00 pm	GitHub repo	Gradescope
hw9	June 18, 22:00 pm	GitHub repo	Gradescope
Final exam	TBA	PrairieLearn (CBTF, in person)	PrairieLearn (CBTF, in person)

Reference Material

Click to expand!

Books

• A Course in Machine Learning (CIML) by Hal Daumé III

• Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Mueller and Sarah Guido.

• An Introduction to Statistical Learning

• The Elements of Statistical Learning (ESL)

• Data Mining: Practical Machine Learning Tools and Techniques (PMLTT)

• Artificial intelligence: A Modern Approach by Russell, Stuart and Peter Norvig.

• Artificial Intelligence 2E: Foundations of Computational Agents (2023) by David Poole and Alan Mackworth (of UBC!).

Online courses

• Machine Learning Crash Course

• Machine Learning (Andrew Ng’s famous Coursera course)

• Foundations of Machine Learning online course from Bloomberg.

• Machine Learning Exercises In Python, Part 1 (translation of Andrew Ng’s course to Python, also relevant for DSCI 561, 572, 563)

Misc

• A Visual Introduction to Machine Learning (Part 1)

• A Few Useful Things to Know About Machine Learning (an article by Pedro Domingos)

• Metacademy (sort of like a concept map for machine learning, with suggested resources)

• Machine Learning 101 (slides by Jason Mayes, engineer at Google)

Enjoy your learning journey in CPSC 330: Applied Machine Learning!