Giter VIP home page Giter VIP logo

cpsc330's Introduction

UBC CPSC 330: Applied Machine Learning (2021W1)

This is the course homepage for CPSC 330: Applied Machine Learning at the University of British Columbia. You are looking at the current version (Sep-Dec 2021). An earlier version from Sep-Dec 2020 can be found here.

Instructor: Varada Kolhatkar

Important links

Deliverable due dates (tentative)

Usually the homework assignments will be due on Mondays (except next week) and will be released on Tuesdays.

Assessment Due date Where to find? Where to submit?
Syllabus quiz Sept 14, 11:59pm Canvas Canvas
hw1 Sept 14, 11:59pm Github repo Gradescope
hw2 Sept 20, 11:59pm Github repo Gradescope
hw3 Oct 04, 11:59pm Github repo Gradescope
hw4 Oct 13, 11:59pm
Oct 15, 11:59pm
Github repo Gradescope
hw5 Oct 25, 11:59pm Oct 27, 11:59pm Github repo Gradescope
Midterm Oct 28, during class time Canvas Canvas
hw6 Nov 15, 11:59pm Github repo Gradescope
hw7 Nov 15, 11:59pm
Nov 17, 11:59pm
Github repo Gradescope
hw8 Nov 22, 11:59pm Github repo Gradescope
hw9 Nov 29, 11:59pm Github repo Gradescope
Final exam TBD Canvas Canvas

Lecture schedule (tentative)

Live lectures: The lectures will be in-person in Hugh Dempster Pavilion (DMP) 110 from 11am to 12:20pm. See the Calendar for more details. The live lecture recordings will be available via Canvas.

Lectures:

  • Try to 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 in a draft form. Once they are finalized, I'll post them on the Jupyter book.
Date Topic Assigned videos and datasets vs. CPSC 340
Sep 7 UBC Imagine Day - no class
Sep 9 Course intro 📹
  • Pre-watch: None
  • During lecture: 1.0
  • n/a
    Part I: ML fundamentals and preprocessing
    Week 2 datasets:
  • grade prediction toy dataset
  • Canada USA cities toy dataset
  • Sep 14 Decision trees 📹
  • Pre-watch: 2.1, 2.2
  • During lecture: 2.3, 2.4
  • less depth
    Sep 16 ML fundamentals 📹
  • Pre-watch: 3.1, 3.2
  • During lecture: 3.3, 3.4
  • similar
    Week 3 datasets:
  • California housing
  • Spotify Song Attributes
  • Sep 21 $k$-NNs and SVM with RBF kernel 📹
  • Pre-watch: 4.1, 4.2
  • During lecture: 4.3, 4.4
  • less depth
    Sep 23 Preprocessing, sklearn pipelines 📹
  • Pre-watch: 5.1, 5.2
  • During lecture: 5.3, 5.4
  • more depth
    Week 4 dataset:
  • California housing
  • Sep 28 More preprocessing, sklearn ColumnTransformer, text features 📹
  • Pre-watch: 6.1, 6.2
  • more depth
    Sep 30 Truth and reconciliation day - no class
    Week 5 datasets:
  • IMDB movie review
  • Oct 5 Linear models 📹
  • Pre-watch: 7.1, 7.2, 7.3
  • less depth
    Oct 7 Lecture canceled
    Week 6 datasets:
  • Spotify Song Attributes
  • Credit Card Fraud Detection
  • Oct 12 Hyperparameter optimization, overfitting the validation set 📹
  • Videos: 8.1,8.2
  • different
    Oct 14 Evaluation metrics for classification 📹
  • Videos: 9.2,9.3,9.4
  • more depth
    Week 7 datasets:
  • Kaggle House Prices data set
  • Adult Census Income
  • Oct 19 Regression metrics 📹
  • Pre-watch: 10.1
  • more depth on metrics less depth on regression
    Oct 21 Ensembles 📹
  • Pre-watch: 11.1,11.2
  • similar
    Week 8 datasets:
  • Adult Census Income
  • Oct 26 feature importances, model interpretation 📹
  • Pre-watch: 12.1,12.2
  • feature importances is new, feature engineering is new
    Oct 28 Midterm
    Week 9 datasets:
  • Credit Card Dataset for Clustering
  • Nov 2 Feature engineering and feature selection None less depth
    Part II: Unsupervised learning, transfer learning, different learning settings
    Nov 4 Clustering 📹
  • Pre-watch: 14.1,14.2,14.3
  • less depth
    Week 10 datasets:
  • Jester 1.7M jokes ratings dataset
  • Nov 9 Simple recommender systems less depth
    Nov 11 Midterm break - no class
    Week 11 datasets:
  • Nov 16 Text data, embeddings, topic modeling 📹
  • Pre-watch: 16.1,16.2
  • new
    Nov 18 Neural networks and computer vision less depth
    Week 12 datasets:
  • Nov 23 Time series data new
    Nov 25 Survival analysis new
    Part III: Communication, ethics, deployment
    Week 13 datasets:
  • Nov 30 Communication new
    Dec 2 Ethics new
    Week 14 datasets:
  • Dec 7 Model deployment and conclusion new

    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 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 welcome in our class.

    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:

    • All course notes will be provided online.
    • All homework assignments can be done and handed in online.
    • All exams will be held online.
    • Most of the class activity will be video recorded and will be made available to you.
    • 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.

    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.

    cpsc330's People

    Contributors

    kvarada avatar mgelbart avatar qianqianf avatar dependabot[bot] avatar

    Recommend Projects

    • React photo React

      A declarative, efficient, and flexible JavaScript library for building user interfaces.

    • Vue.js photo Vue.js

      🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

    • Typescript photo Typescript

      TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

    • TensorFlow photo TensorFlow

      An Open Source Machine Learning Framework for Everyone

    • Django photo Django

      The Web framework for perfectionists with deadlines.

    • D3 photo D3

      Bring data to life with SVG, Canvas and HTML. 📊📈🎉

    Recommend Topics

    • javascript

      JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

    • web

      Some thing interesting about web. New door for the world.

    • server

      A server is a program made to process requests and deliver data to clients.

    • Machine learning

      Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

    • Game

      Some thing interesting about game, make everyone happy.

    Recommend Org

    • Facebook photo Facebook

      We are working to build community through open source technology. NB: members must have two-factor auth.

    • Microsoft photo Microsoft

      Open source projects and samples from Microsoft.

    • Google photo Google

      Google ❤️ Open Source for everyone.

    • D3 photo D3

      Data-Driven Documents codes.