Update documentation

UBC-MDS · Mar 14, 2024 · 99eca3f · 99eca3f
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diff --git a/.buildinfo b/.buildinfo
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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 6c423f595ed87fe5c80aa31b3990d3cc
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/.nojekyll b/.nojekyll
diff --git a/LICENSE.html b/LICENSE.html
diff --git a/README.html b/README.html
diff --git a/_downloads/f0662b3278412e4d7bf4ad95fc9b67bd/env-dsci-563.yml b/_downloads/f0662b3278412e4d7bf4ad95fc9b67bd/env-dsci-563.yml
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+# install with conda env create -f env-dsci-563.yml
+
+name: '563'
+channels:
+  - conda-forge
+  - pytorch
+  - bioconda
+  - plotly
+  - defaults
+dependencies:
+  - ipykernel
+  - scipy
+  - pandas
+  - matplotlib
+  - altair
+  - seaborn
+  - plotly
+  - pyani
+  - scikit-learn>=1.0.2
+  - requests
+  - pytorch
+  - torchvision
+  - black
+  - flake8  
+  - jinja2
+  - wget
+  - pip
+  - pandas-profiling
+  - spacy
+  - wikipedia
+  - gensim
+  - nltk
+  - ipywidgets
+  - pip:
+    - torch-summary  
+    - sentence-transformers>=2.1.0    
+    - psutil>=5.7.2
+    - yellowbrick>=1.3.*
+    - mglearn>=0.1.9
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diff --git a/_sources/LICENSE.md b/_sources/LICENSE.md
@@ -0,0 +1,44 @@
+# LICENSE
+
+## Instructional Material
+
+All instructional material in this repository is made available under the **Attribution 4.0 International** ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)). 
+
+
+This is a human-readable summary of (and not a substitute for) the [license](https://creativecommons.org/licenses/by/4.0/legalcode). 
+
+You are free to:
+* **Share** -- copy and redistribute the material in any medium or format
+* **Adapt** -- remix, transform, and build upon the material
+    for any purpose, even commercially.
+
+
+The licensor cannot revoke these freedoms as long as you follow the license terms.
+
+
+Under the following terms:
+
+- **Attribution** -- You must give appropriate credit (mentioning that your work is derived from work that is Copyright © Varada Kolhatkar and Mike  Gelbart), provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
+
+- **No additional restrictions** -- You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
+
+Notices:
+
+- You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
+- No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
+
+
+## Software
+
+Except where otherwise noted, the example programs and other software provided in this repository are made available under the MIT license.
+
+MIT License
+
+Copyright (c) 2024 Varada Kolhatkar, Rodolfo Lourenzutti, and Mike Gelbart
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/_sources/README.md b/_sources/README.md
@@ -0,0 +1,196 @@
+
+![](lectures/img/563_banner.png)
+
+## Important links 
+
+- [Course Jupyter book](https://pages.github.ubc.ca/mds-2023-24/DSCI_563_unsup-learn_students/README.html)
+- [Course GitHub page](https://github.ubc.ca/MDS-2023-24/DSCI_563_unsup-learn_students)
+- [Slack Channel](https://ubc-mds.slack.com/messages/563_unsup-learn)
+- [Canvas](https://canvas.ubc.ca/courses/130310)
+- [Gradescope](https://www.gradescope.ca/courses/12077)
+- [YouTube videos](https://www.youtube.com/playlist?list=PLHofvQE1VlGtZoAULxcHb7lOsMved0CuM)
+- [Class + office hours calendar](https://ubc-mds.github.io/calendar/)
+## 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. 
+
+<details>
+  <summary>Click to expand!</summary>  
+
+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.  
+</details>
+
+
+## Deliverables
+
+<details>
+  <summary>Click to expand!</summary>
+
+The following deliverables will determine your course grade:
+
+| Assessment           | Weight  | Where to submit|
+| :---:                | :---:   |:---:  | 
+| Lab Assignment 1     | 12%     | [Gradescope](https://www.gradescope.ca/courses/12077) |
+| Lab Assignment 2     | 12%     | [Gradescope](https://www.gradescope.ca/courses/12077) 
+| Lab Assignment 3     | 12%     | [Gradescope](https://www.gradescope.ca/courses/12077) |
+| Lab Assignment 4     | 12%     | [Gradescope](https://www.gradescope.ca/courses/12077) |
+| Class participation  |  2%     | [iClicker Cloud]() |
+| Quiz 1               | 25%     | [Canvas](https://canvas.ubc.ca/courses/106525)     |
+| Quiz 2               | 25%     | [Canvas](https://canvas.ubc.ca/courses/106525)     |
+
+See [Calendar](https://ubc-mds.github.io/calendar/) for the due dates. 
+</details>
+
+## Teaching team
+<details>
+  <summary>Click to expand!</summary>
+
+| Role | Name  | 
+| :------: | :---: |
+| Lecture instructor | Varada Kolhatkar |
+| Lab instructor | Varada Kolhatkar |
+| Teaching assistant | Ngoc Bui|
+| Teaching assistant | Mohit Pandey |
+| Teaching assistant | Negar Sadrzadeh |
+| Teaching assistant | Jordan Yu |
+
+</details>  
+
+## Lectures 
+
+### Format
+<details>
+  <summary>Click to expand!</summary>
+
+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. 
+</details>
+
+### Lecture Schedule
+
+This course occurs during **Block 5** in the 2021/22 school year. 
+
+| Lecture  | Topic  | Assigned videos  | Resources and optional readings |
+|-------|------------|-----------|-----------|
+| 0     | [Course Information](lectures/00_course-information.ipynb) | | |
+| 1     | [K-Means and intro to GMMs](lectures/01_lecture-k-means.ipynb)  | 📹  <li> Videos: [14.1](https://youtu.be/caAuUAXwpb8), [14.2](https://youtu.be/s6AvSZ1_l7I),[14.3](https://youtu.be/M5ilrhcL0oY)| <li>[`sklearn` clustering documentation](https://scikit-learn.org/stable/modules/clustering.html)</li><li>["Spaghetti Sauce" talk by Malcom Gladwell](https://www.ted.com/talks/malcolm_gladwell_on_spaghetti_sauce?language=en)</li><li>[Visualizing-k-means-clustering](https://www.naftaliharris.com/blog/visualizing-k-means-clustering/)</li><li>[Visualizing K-Means algorithm with D3.js](http://tech.nitoyon.com/en/blog/2013/11/07/k-means/)</li><li>[Clustering with Scikit with GIFs](https://dashee87.github.io/data%20science/general/Clustering-with-Scikit-with-GIFs/)</li>|
+| 2    | [DBSCAN and Hierarchical Clustering](lectures/02_DBSCAN-hierarchical.ipynb)  | 📹  <li> Videos: [15.1](https://youtu.be/1ZwITQyWpkY), [15.2](https://youtu.be/T4NLsrUaRtg), [15.3](https://youtu.be/NM8lFKFZ2IU) | <li>Comparison of [sklearn clustering algorithms](https://scikit-learn.org/stable/modules/clustering.html#overview-of-clustering-methods)</li><li>[DBSCAN Visualization](https://www.naftaliharris.com/blog/visualizing-dbscan-clustering/)</li><li>[Clustering with Scikit with GIFs](https://dashee87.github.io/data%20science/general/Clustering-with-Scikit-with-GIFs/)</li> | 
+| 3    | [Dimensionality Reduction Intro](lectures/03_PCA-intro.ipynb) | 📹  <li> Videos: [17.1](https://youtu.be/r-DwXpg1YDI), [17.2](https://youtu.be/33TRSSuzALw), [17.3](https://youtu.be/g5w3o1TE6hU)</li> | <li>[PCA visualization](https://setosa.io/ev/principal-component-analysis/)</li><li>[Introduction to Machine Learning with Python book Chapter 3](https://learning.oreilly.com/library/view/introduction-to-machine/9781449369880/ch03.html)</li><li>[Mike's PCA video from CPSC 340](https://www.youtube.com/watch?v=7cBkOC_UD4o&list=PLWmXHcz_53Q02ZLeAxigki1JZFfCO6M-b&index=25&t=0s)</li><li>[StatQuest PCA video](https://www.youtube.com/watch?v=FgakZw6K1QQ&feature=youtu.be)</li> |
+| 4    | [More PCA, LSA, NMF, Autoencoders](lectures/04_LSA-NMF-AE.ipynb) | No videos | 
+|   5   | [Word Vectors, Word Embeddings](lectures/05_word-embeddings.ipynb) | 📹  <li> Videos: [18.1](https://youtu.be/7nGGogNUrtg), [18.2](https://youtu.be/aj8OWol-H2I), [18.3](https://youtu.be/rWoA-IKGDa8)</li> | Word2Vec papers: <li>[Distributed representations of words and phrases and their compositionality](https://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf)</li> <li>[Efficient estimation of word representations in vector space](https://arxiv.org/pdf/1301.3781.pdf)</li> <li>[word2vec Explained](https://arxiv.org/pdf/1402.3722.pdf)</li><li>[Debiasing Word Embeddings](http://papers.nips.cc/paper/6228-man-is-to-computer-programmer-as-woman-is-to-homemaker-debiasing-word-embeddings.pdf)</li>|
+|   6   | [Using Word Embeddings, Manifold Learning](lectures/06_more-word2vec-tsne.ipynb) | No videos | <li>[t-SNE tutorial](https://github.com/oreillymedia/t-SNE-tutorial)</li><li>[How to use t-SNE effectively](https://distill.pub/2016/misread-tsne/)</li><li>[LargeVis](https://github.com/elbamos/largeVis)</li><li>[UMAP](https://github.com/lmcinnes/umap)</li> |
+| 7    | Recommender Systems I | No videos | <li>[Collaborative filtering for recommendation systems in Python, by N. Hug](https://www.youtube.com/watch?v=z0dx-YckFko)</li><li>[How Netflix’s Recommendations System Works](https://help.netflix.com/en/node/100639)</li>|
+| 8    | Recommender Systems II | No videos | <li>[SVDfeature](https://www.jmlr.org/papers/v13/chen12a.html)</li>|
+
+
+
+### Datasets
+Here is the list of [Kaggle](https://www.kaggle.com/) datasets we'll use in the lectures. 
+- A small subset of [200 Bird Species with 11,788 Images](https://www.kaggle.com/datasets/veeralakrishna/200-bird-species-with-11788-images) (available [here](https://github.ubc.ca/mds-2021-22/datasets/blob/master/data/birds.zip))
+- A tiny subset of [Food-101](https://www.kaggle.com/datasets/kmader/food41?select=food_c101_n10099_r32x32x1.h5)
+(available [here](https://github.ubc.ca/mds-2021-22/datasets/blob/master/data/food.zip))
+- [Credit Card Dataset for Clustering](https://www.kaggle.com/arjunbhasin2013/ccdata)
+- [Countries of the World](https://www.kaggle.com/fernandol/countries-of-the-world)
+- [Airline Sentiment](https://www.kaggle.com/jaskarancr/airline-sentiment-dataset)
+- [Jester 1.7M jokes ratings dataset](https://www.kaggle.com/vikashrajluhaniwal/jester-17m-jokes-ratings-dataset)
+- [Amazon ratings data](http://snap.stanford.edu/data/amazon/productGraph/categoryFiles/ratings_Patio_Lawn_and_Garden.csv)
+
+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](env-dsci-563.yml). 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`. If you're unable to see the environment in Jupyter, you might have to install the kernel manually. See the documentation [here](https://ipython.readthedocs.io/en/stable/install/kernel_install.html). For more details on this, refer to your [521 lecture 7](https://pages.github.ubc.ca/MDS-2023-24/DSCI_521_platforms-dsci_students/lectures/7-virtual-environments.html#).
+
+I've only attempted to install this environment file on a few machines, and you may encounter issues with certain packages from the `yaml` file when executing the commands above. This is not uncommon and may suggest that the specified package version is not yet available for your operating system via `conda`. When this occurs, you have a couple of options:
+
+1. Modify the local version of the `yaml` file to remove the line containing that package.
+2. Create the environment without that package. 
+3. Activate the environment and install the package manually either with `conda install` or `pip install` in 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
+<details>
+  <summary>Click to expand!</summary>
+
+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 post a message on our course channel 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](https://ubc-mds.github.io/policies/)). 
+- 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. 
+
+</details>
+
+
+## Reference Material
+<details>
+    <summary>Click to expand!</summary>   
+
+### Books
+* [A Course in Machine Learning (CIML)](http://ciml.info/) 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)](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)
+* [ML:APP](http://www.cs.ubc.ca/~murphyk/MLbook/index.html), 
+* [LFD](http://amlbook.com/), 
+* [AI:AMA](http://aima.cs.berkeley.edu/)
+* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf)
+
+### Linear algebra review
+
+- There are a bunch of suggestions [here](https://ubc-mds.github.io/resources_pages/learning_resources/). We particularly recommend [essence of linear algebra](https://www.youtube.com/watch?v=kjBOesZCoqc&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) (YouTube series) and
+[Immersive linear algebra](http://immersivemath.com/ila/index.html) (interactive e-book).
+- [Introduction to Linear Algebra for Applied Machine Learning with Python](https://pabloinsente.github.io/intro-linear-algebra)
+
+### Online courses
+
+* [Mike's CPSC 340](https://ubc-cs.github.io/cpsc340/)
+* [Machine Learning](https://www.coursera.org/learn/machine-learning) (Andrew Ng's famous Coursera course)
+* [Foundations of Machine Learning](https://bloomberg.github.io/foml/#home) online course from Bloomberg.
+* [Machine Learning Exercises In Python, Part 1](http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/) (translation of Andrew Ng's course to Python, also relevant for DSCI 561, 572, 563)
+
+</details> 
+
+## Policies
+
+Please see the general [MDS policies](https://ubc-mds.github.io/policies/).
diff --git a/_sources/attribution.md b/_sources/attribution.md
@@ -0,0 +1,3 @@
+# Attributions 
+
+Many excellent people have contributed to this material. The original content was put together by [Mark Schmidt](https://www.cs.ubc.ca/~schmidtm/) as powerpoint slides. Since then Varada Kolhatkar has contributed and developed the material further. Some of the material is borrowed from CPSC 330, which was first developed by Mike Gelbart and then modified and updated by Varada Kolhatkar.