Important links
Course learning outcomes
This course is about identifying underlying structure in data. We will talk about clustering, data representation (e.g., dimensionality reduction and word embeddings), and recommendation systems.
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By the end of the course, students are expected to be able to
- Explain the unsupervised paradigm.
- Explain the intuition behind clustering and use appropriate clustering algorithms for applications such as image clustering and document clustering.
- Interpret the results obtained after applying clustering.
- Explain the intuition behind dimensionality reduction.
- Broadly explain and use linear dimensionality reduction techniques such as PCA, LSA, and NMF.
- Explain the intuition of word2vec model to create word embeddings.
- Train your own word embeddings and use pre-trained word embeddings.
- Explain the recommender systems problem.
- Broadly explain and use two common approaches to recommender systems: collaborative filtering and content-based filtering.
- Explain consequences of using recommender systems.
Deliverables
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The following deliverables will determine your course grade:
| Assessment | Weight | Where to submit |
|---|---|---|
| Lab Assignment 1 | 15% | Gradescope |
| Lab Assignment 2 | 15% | Gradescope |
| Lab Assignment 3 | 15% | Gradescope |
| Lab Assignment 4 | 15% | Gradescope |
| Quiz 1 | 20% | Canvas |
| Quiz 2 | 20% | Canvas |
See Calendar for the due dates.
Teaching team
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| Role | Name |
|---|---|
| Lecture instructor | Varada Kolhatkar |
| Lab instructor | Varada Kolhatkar |
| Teaching assistant | Daniel Ramandi |
| Teaching assistant | Farnoosh Hashemi |
| Teaching assistant | Glenn Chang |
| Teaching assistant | Harsh Sharma |
| Teaching assistant | Shan Lu |
Lectures
Format
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This class will follow a semi-flipped classroom format. For four out of the eight lectures, you will be required to watch a few pre-recorded videos (~30 to ~50 min long) before the lecture. All videos are available on YouTube and are linked in the Lecture Schedule below. During lectures, I'll summarize the content from videos but I'll assume that you understand the basic concepts from the videos and we will focus on more advanced material, iClicker exercises, discussions, demos, and class activities. It's optional but highly recommended to download the appropriate datasets provided below and put them under your local lectures/data directory, and run the lecture Jupyter notebooks on your own and experiment with the code.
Lecture Schedule
This course occurs during Block 5 in the 2021/22 school year.
Datasets
Here is the list of Kaggle datasets we'll use in the lectures.
- A small subset of 200 Bird Species with 11,788 Images (available here)
- A tiny subset of Food-101 (available here)
- Credit Card Dataset for Clustering
- Countries of the World
- Airline Sentiment
- Jester 1.7M jokes ratings dataset
- Amazon ratings data
If you want to be extra prepared, you may want to download these datasets in advance and save them under the lectures/data directory in your local copy of the repository.
Labs
During labs, you will be given time to work on your own or in groups. There will be a lot of opportunity for discussion and getting help during lab sessions.
Installation
We are providing you with a conda environment file which is available here. You can download this file and create a conda environment for the course and activate it as follows.
conda env create -f env-dsci-563.yml
conda activate 563
In order to use this environment in Jupyter, you will have to install nb_conda_kernels in the environment where you have installed Jupyter (typically the base environment). You will then be able to select this new environment in Jupyter. For more details on this, refer to "Making environments work well with JupyterLab section" in your 521 lecture 6.
I've only tried installing this environment file on a couple of machines, and it's possible that you will encounter problems with some of the packages from the yml file when you run the commands above. This is not unusual. It often means that the package with the given version is not available for your operating system via conda yet. There are a couple of options for you when this happens:
- Get rid of the line with that package from the
ymlfile. - Create the environment without that package.
- Activate the environment and install the package manually either with
conda installorpip installin the environment.
Note that this is not a complete list of the packages we'll be using in the course and there might be a few packages you will be installing using conda install later in the course. But this is a good enough list to get you started.
Course communication
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We all are here to help you learn and succeed in the course and the program. Here is how we'll be communicating with each other during the course.
Clarifications on the lecture notes or lab questions
If there is any clarification on the lecture material or lab questions, I'll open an issue in the course repository and tag you. It is your responsibility to read the messages whenever you are tagged. (I know that there are too many things for you to keep track of. You do not have to read all the messages but please make sure to carefully read the messages whenever you are tagged.)
Questions on lecture material or labs
If you have questions about the lecture material or lab questions please post them on the course Slack channel rather than direct messaging me or the TAs. Here are the advantages of doing so:
- You'll get a quicker response.
- Your classmates will benefit from the discussion.
When you ask your question on the course channel, please avoid tagging the instructor unless it's specific for the instructor (e.g., if you notice some mistake in the lecture notes). If you tag a specific person, other teaching team members or your colleagues are discouraged to respond. This will decrease the response rate on the channel.
Please use some consistent convention when you ask questions on Slack to facilitate easy search for others or future you. For example, if you want to ask a question on Exercise 3.2 from Lab 1, start your post with the label lab1-ex2.3. Or if you have a question on lecture 2 material, start your post with the label lecture2. Once the question is answered/solved, you can add "(solved)" tag before the label (e.g., (solved) lab1-ex2.3). Do not delete your post even if you figure out the answer on your own. The question and the discussion can still be beneficial to others.
Questions related to grading
For each deliverable, after I return grades, I'll let you know who has graded what in our course Slack by opening an issue in the course GitHub repository. If you have questions related to grading
- First, make sure your concerns are reasonable (read the "Reasonable grading concerns" policy).
- If you believe that your request is reasonable, open a regrade request on Gradescope.
- If you are unable to resolve the issue with the TA, send a Slack message to the instructor, including the appropriate TA in the conversation.
Questions related to your personal situation or talking about sensitive information
I am open for a conversation with you. If you want to talk about anything sensitive, please direct message me on Slack (and tag me) rather than posting it on the course channel. It might take a while for me to get back to you, but I'll try my best to respond as soon as possible.
Working during the COVID-19 global pandemic
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We are working together on this course during this transition period between hybrid to in-person teaching and learning. 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! Let's try to be open with each other and help each other.
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, getting back to in-person teaching and learning, 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
Please read Covid Campus Rules.
Masks: This class is going to be in person. UBC no longer requires students, faculty and staff to wear non-medical masks, but continues to recommend that masks be worn in indoor public spaces.
Your personal health: If you are ill or believe you have COVID-19 symptoms or been exposed to SARS-CoV-2 use the Thrive Health self-assessment tool for guidance, or download the BC COVID-19 Support App for iOS or Android device and follow the instructions provided. Follow the advice from Public Health.
Stay home if you have recently tested positive for COVID-19 or are required to quarantine. You can check this website to find out if you should self-isolate or self-monitor. If you are unable to submit a deliverable on time or unable to appear for an in-person quiz, check out MDS policies on academic concession and remote quiz requests.
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.
Reference Material
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Books
- A Course in Machine Learning (CIML) by Hal Daumé III (also relevant for DSCI 572, 573, 575, 563)
- Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Mueller and Sarah Guido.
- The Elements of Statistical Learning (ESL)
- ML:APP,
- LFD,
- AI:AMA
- An Introduction to Statistical Learning
Linear algebra review
- There are a bunch of suggestions here. We particularly recommend essence of linear algebra (YouTube series) and Immersive linear algebra (interactive e-book).
- Introduction to Linear Algebra for Applied Machine Learning with Python
Online courses
- Mike's CPSC 340
- 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)
Policies
Please see the general MDS policies.