colebrauer/cpsc330-2021w2

UBC CPSC 330: Applied Machine Learning (2021W2)

Watch out for 👀 (last updated: 25/01/22)

Keep an eye on this paragraph at the top of the readme file, I will try to keep it up to date with urgent to-dos and important things.

Please watch the following videos before next class on Thursday: 6.1, 6.2.

HW3 is out and due next Wednesday, February 2nd at 11:59 PM. You are allowed to work and submit in pairs. Allowed = not mandatory, it is up to your preference. It may be a bit challenging to find a partner remotely, so here is a Piazza post to help your search: https://piazza.com/class/ky0j51i4ud64t5?cid=5

Students who were moved to the course from the waitlist after January 14th can submit the syllabus quiz by this Friday, January 28th. Here is the link to the quiz: https://canvas.ubc.ca/courses/83420/quizzes/453912

Introduction

This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. You are looking at the current version (Jan-Apr 2022). Earlier versions can be found at these links:

• from Sep-Dec 2021
• from Sep-Dec 2020.

Instructor: Giulia Toti

Important links

• Course GitHub page
• Course Jupyter book
• Course videos YouTube channel
• Canvas link
• Syllabus / administrative info
• Piazza (this is where all announcements will be made). Click here to enroll.
• Other course documents
• Past exams

Deliverable due dates (tentative)

Usually the homework assignments will be due on Mondays and will be released on Tuesdays.

Assessment	Due date	Where to find?	Where to submit?
Syllabus quiz	Jan 17, 11:59pm	Canvas	Canvas
hw1	Jan 17, 11:59pm	Github repo	Gradescope
hw2	Jan 24, 11:59pm	Github repo	Gradescope
hw3	Feb 2, 11:59pm	Github repo	Gradescope
hw4	Feb 7, 11:59pm	Github repo	Gradescope
hw5	Feb 14, 11:59pm	Github repo	Gradescope
Midterm	Feb 17, during class time	TBD	TBD
hw6	Mar 7, 11:59pm	Github repo	Gradescope
hw7	Mar 14, 11:59pm	Github repo	Gradescope
hw8	Mar 21, 11:59pm	Github repo	Gradescope
Final exam	TBD	TBD	TBD

Lecture schedule (tentative)

Lectures will be on Tuesday and Thursday from 12:30pm to 2:00pm.

Online lectures: Lectures will be delivered online until January 24th (at least). Look for the Zoom tab on Canvas to find the link to connect to the lectures. Lectures recordings will also be available on Canvas.

Live lectures: In-person lectures will be in Hugh Dempster Pavilion (DMP) 310.

Lectures:

• Watch the "Pre-watch" videos before each lecture.
• I'll be developing lecture notes in this repository. So if you check them before the lecture, they might be unavailable or in a draft form.

Date	Topic	Assigned videos and datasets	vs. CPSC 340
Jan 11	Course intro	📹 Pre-watch: None Recap video (after lecture): 1.0	n/a
	Part I: ML fundamentals and preprocessing
		Week 1 datasets: grade prediction toy dataset Canada USA cities toy dataset
Jan 13	Decision trees	📹 Pre-watch: 2.1, 2.2 After lecture: 2.3, 2.4	less depth
Jan 18	ML fundamentals	📹 Pre-watch: 3.1, 3.2 After lecture: 3.3, 3.4	similar
		Week 2 datasets: California housing Spotify Song Attributes
Jan 20	$k$-NNs and SVM with RBF kernel	📹 Pre-watch: 4.1, 4.2 During lecture: 4.3, 4.4	less depth
Jan 25	Preprocessing, sklearn pipelines	📹 Pre-watch: 5.1, 5.2 During lecture: 5.3, 5.4	more depth
		Week 3 dataset: California housing
Jan 27	More preprocessing, sklearn ColumnTransformer, text features	📹 Pre-watch: 6.1, 6.2	more depth
		Week 4 datasets: IMDB movie review
Feb 1	Linear models	📹 Pre-watch: 7.1, 7.2, 7.3	less depth
		Week 5 datasets: Spotify Song Attributes Credit Card Fraud Detection
Feb 3	Hyperparameter optimization, overfitting the validation set	📹 Videos: 8.1,8.2	different
Feb 8	Evaluation metrics for classification	📹 Videos: 9.2,9.3,9.4	more depth
		Week 6 datasets: Kaggle House Prices data set Adult Census Income
Feb 10	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 17	Midterm
Feb 20-26	Reading week (no classes)
		Week 7 datasets: Adult Census Income Credit Card Dataset for Clustering
Mar 2	feature importances, model interpretation	📹 Pre-watch: 12.1,12.2	feature importances is new, feature engineering is new
Mar 4	Feature engineering and feature selection	None	less depth
	Part II: Unsupervised learning, transfer learning, different learning settings
Mar 8	Clustering	📹 Pre-watch: 14.1,14.2,14.3	less depth
		Week 9 datasets: Jester 1.7M jokes ratings dataset
Mar 10	Simple recommender systems		less depth
Mar 15	Text data, embeddings, topic modeling	📹 Pre-watch: 16.1,16.2	new
Mar 17	Neural networks and computer vision		less depth
Mar 22	Time series data	(Optional) Humour: The Problem with Time & Timezones	new
Mar 24	Survival analysis	📹 (Optional but highly recommended)Calling Bullshit 4.1: Right Censoring	new
	Part III: Communication, ethics, deployment
Mar 29	Ethics	📹 (Optional but highly recommended) Calling BS videos Chapter 5 (6 short videos, 50 min total) The ethics of data science	new
Mar 31	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 5	Model deployment and conclusion		new
Apr 7	Buffer (TBD)

Working during the COVID-19 global pandemic

We are working together on this course during a global pandemic. Everyone is struggling to some extent. If you tell me you are having trouble, I am not going to judge you or think less of you. I hope you will extend me the same grace!

Here are some ground rules:

• If you are unable to submit a deliverable on time, please reach out before the deliverable is due.
• If you need extra support, the teaching team is here to work with you. Our goal is to help each of you succeed in the course.
• If you are struggling with the material, the new hybrid teaching format, or anything else, please reach out. I will try to find time and listen to you empathetically.
• If I am unable to help you, I might know someone who can. UBC has some great student support resources.

Covid Safety at UBC

Masks: This class is going to be (mostly) in person. Masks are required indoors, including in classrooms, as per the BC Public Health Officer orders. For the purposes of this order, the term "masks" refers to medical and non-medical masks that cover our noses and mouths. Masks are a primary tool to make it harder for Covid-19 to find a new host. You will need to wear a medical or non-medical mask anytime you are indoors at UBC, for your own protection, and the safety and comfort of everyone else in the class. Please do not eat in the classroom. If you need to drink water/coffee/tea/etc, please keep your mask on between sips. Please note that there are some people who cannot wear a mask. These individuals are equally welcomed in our class.

Seating in class: To reduce the risk of Covid transmission, please sit in a consistent area of the classroom each day. This will minimize your contacts and will still allow for the pedagogical methods planned for this class to help your learning.

Questions after class: We realize that many of you may have questions immediately after class and that this is a convenient time to ask them. However, for our in-person sections this term, we ask that you do not approach the instructor after class. Please vacate the room as soon as possible, to allow the next group of students to enter. If you have questions about lecture content or operational aspects of the course, please post them to Piazza or ask during office hours.

Vaccination: If you have not yet had a chance to get vaccinated against Covid-19, vaccines are available to you, free, and on campus [http://www.vch.ca/covid-19/covid-19-vaccine]. The higher the rate of vaccination in our community overall, the lower the chance of spreading this virus. You are an important part of the UBC community. Please arrange to get vaccinated if you have not already done so.

COVID-19 testing: UBC will require COVID-19 testing for all students, faculty and staff, with exemptions provided for those who are vaccinated against COVID-19: [https://news.ubc.ca/2021/08/26/ubc-implements-vaccine-declaration-and-rapid-testing-for-covid-19/]

Your personal health: If you're sick, it's important that you stay home – no matter what you think you may be sick with (e.g., cold, flu, other). A daily self-health assessment is required before attending campus. Every day, before leaving home, complete the self-assessment for Covid symptoms using this tool.

Stay home if you have Covid symptoms, have recently tested positive for Covid, or are required to quarantine. You can check this website to find out if you should self-isolate or self-monitor.

Your precautions will help reduce risk and keep everyone safer. In this class, the marking scheme is intended to provide flexibility so that you can prioritize your health and still be able to succeed:

• Attendance in classes and tutorials is not graded (although obviously beneficial when it is safe to attend).
• All course notes will be provided online.
• All homework assignments can be done and handed in online.
• Video recordings of class activities will be made available to you when possible/appropriate.
• Before each class, I'll also try to post some videos on YouTube to facilitate hybrid learning.
• There will be at least a few office hours which will be held online.

If sick on an exam day: If you are sick on a midterm exam day, please contact the instructor through Piazza as soon as you are confident you should not come to the scheduled exam. We would strongly prefer that you contact us to make an alternate arrangement than for you to come to the exam while you are ill. If you do show up for an exam and you are clearly ill, you will not be able to write the exam and we will make alternate arrangements with you. It is much better for you to email ahead of time and not attend. Remember to include your full name and student number in your message.

If you are sick on a final exam day, do not attend the exam. You must apply for deferred standing (an academic concession) through Science Advising no later than 48 hours after the missed final exam/assignment. Students who are granted deferred standing write the final exam/assignment at a later date. Learn more and find the application online: https://science.ubc.ca/students/advising/concession

For additional information about academic concessions, see the UBC policy here:http://www.calendar.ubc.ca/vancouver/index.cfm?tree=3,329,0,0

Official statement from UBC regarding the online learning experience:

During this pandemic, the shift to online learning has greatly altered teaching and studying at UBC, including changes to health and safety considerations. Keep in mind that some UBC courses might cover topics that are censored or considered illegal by non-Canadian governments. This may include, but is not limited to, human rights, representative government, defamation, obscenity, gender or sexuality, and historical or current geopolitical controversies. If you are a student living abroad, you will be subject to the laws of your local jurisdiction, and your local authorities might limit your access to course material or take punitive action against you. UBC is strongly committed to academic freedom, but has no control over foreign authorities (please visit http://www.calendar.ubc.ca/vancouver/index.cfm?tree=3,33,86,0 for an articulation of the values of the University conveyed in the Senate Statement on Academic Freedom). Thus, we recognize that students will have legitimate reason to exercise caution in studying certain subjects. If you have concerns regarding your personal situation, consider postponing taking a course with manifest risks, until you are back on campus or reach out to your academic advisor to find substitute courses. For further information and support, please visit: http://academic.ubc.ca/support-resources/freedom-expression.