Syllabus in PDF form - Current as of Sep 12, 2019.
Pratt Institute School of Information
INFO 697-03: Machine Learning
Fall 2019 Class Hours: Tuesdays, 6:30p – 9:20p
Machine learning is a rapidly growing field that develops algorithms for tasks such as data classification and prediction. These algorithms are programmed to operate and adjust themselves independently of human intervention (i.e., to learn), allowing data work to occur quickly and at scale. Machine learning is a key technology behind the automation across many social areas today, often branded AI.
This course offers an introduction to machine learning as a practical tool that we can use, and as a technological field with social implications. We will learn about key concepts in machine learning; survey a few key machine learning techniques, such as supervised methods for machine learning (regression and classification), which attempt to map data onto desired outputs, and unsupervised methods (clustering and association), which attempt to find structure within data itself; use openly available tools to implement these techniques on text and image data; learn how to assess the effectiveness of different techniques on particular datasets; and discuss basic issues that confront all machine learning methods.
Readings, class discussions, and hands-on sessions will be complemented by guest lectures (TBD) from machine learning practitioners. Students will be assessed via a final project developed throughout the course, in addition to the project proposal, presentation, class participation, and lab assignments.
This syllabus is a living document; expect it to evolve over the course of the semester. All changes will be communicated in class and the updated syllabus will be uploaded on LMS. Since this is a new course, your participation and input will be crucial in shaping it to your needs. Feel free to ask questions and give feedback or suggestions, in person or via email, as we move into the semester.
The goals of this course are to:
- introduce students to key concepts and some common techniques in machine learning, as well as openly available tools
- help students to develop technical and critical thinking skills regarding machine learning
- enable students to conduct a machine learning experiment and communicate the result of their project
Student Learning Outcomes:
By the end of the course, students will be able to:
- describe different machine learning methods, including their limitations
- select an appropriate machine learning method for a given use case
- implement machine learning algorithms and assess their performance
- execute a machine learning experiment using openly available tools
- support the design of their experiment by discussing both the technical aspect and the topic’s significance
Course Schedule and Readings:
Week 1 - 8/27: Introduction
Class overview; Introduction to machine learning
Lab: Getting started with Python
Week 2 - 9/3: Machine learning, data, programming
- Meredith Broussard, Artificial Unintelligence, ch.2-3 (13-39) – via LMS
- Liza Daly, “AI Literacy: The basics of machine learning” https://worldwritable.com/ai-literacy-the-basics-of-machine-learning-2e20f93e34b4
- Siddhartha Mukherjee, “AI Versus MD” http://web.archive.org/web/20170427141526/http://www.newyorker.com/magazine/2017/04/03/ai-versus-md
- Gideon Lewis-Kraus, “The Great A.I. Awakening” http://web.archive.org/web/20161215073155/https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html
- Paul Ford, “What is Code?” https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/
- Douglas Hofstadter, “The Shallowness of Google Translate” https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/
Lab: Working with data in Python
Week 3 - 9/10: Classification
Due: Lab assignment #1 (submit by 9/9)
- Broussard, Artificial Unintelligence, ch.7 (87-119) – via LMS
- Stephanie Yee and Tony Chu, “A visual introduction to machine learning” http://www.r2d3.us/visual-intro-to-machine-learning-part-1/
- Gene Kogan and Francis Tseng, “Fundamentals, introduction to machine learning” https://github.com/ml4a/ml4a-guides/blob/master/notebooks/fundamentals.ipynb
Lab: Introduction to scikit-learn; classifiers
Week 3 code notebook: google colab link
Week 4 - 9/17: Classification continued; regression; gradient descent
- 3Blue1Brown, “Gradient descent, how neural networks learn | Deep learning, chapter 2” https://www.youtube.com/watch?v=IHZwWFHWa-w
- Chris Deotte, “Classifier Playground” http://www.ccom.ucsd.edu/~cdeotte/programs/classify.html
- Khan Academy, “Introduction to trend lines” (MOOC module, playlist) https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data/introduction-to-trend-lines/v/fitting-a-line-to-data
- Khan Academy, “Least-squares regression equations” (MOOC module, playlist) https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data/regression-library/v/introduction-to-residuals-and-least-squares-regression
Lab: Classifiers continued; regression; gradient descent
Week 5 - 9/24: Model, software, abstraction; data prep and features; evaluation
Due: Lab assignment #2 (submit by 9/23)
- Alex Galloway, “The Computational Decision” http://cultureandcommunication.org/galloway/the-computational-decision
- Cathy O’Neil, Weapons of Math Destruction, introduction, ch.1 & conclusion (1-31, 199-218) – via LMS
- Johanna Drucker, “Humanities Approaches to Graphical Display” http://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html
- Os Keyes, “Counting the Countless” https://reallifemag.com/counting-the-countless/
- Wendy Hui Kyong Chun, “On Software, or the Persistence of Visual Knowledge” – via LMS
- Lisa Gitelman (ed.), “Raw Data” Is an Oxymoron, introduction (1-14) http://raley.english.ucsb.edu/wp-content/Engl800/RawData-excerpts.pdf
- Mimi Onuoha, “On Missing Datasets” https://github.com/MimiOnuoha/missing-datasets
- Nick Seaver, “Knowing Algorithms” https://digitalsts.net/essays/knowing-algorithms/
Lab: Features and parameters; model evaluation; data prep
Week 6 - 10/1: Project planning
In the first part of this class, students will share project ideas and give each other feedback.
- Example projects and resources: https://www.are.na/achim-koh/ml-design-ish (The linked list is a preliminary one and will be updated; also, the examples are meant primarily as inspirations, and do not indicate what the final project should look like)
Lab: Data prep continued
Week 7 - 10/8: NO CLASS - Midterm break
Week 8 - 10/15: Neural networks
Due: Project proposal (submit by 10/13)
- 3Blue1Brown, “Neural Networks” (YouTube playlist) https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
- Chris Deotte, “Neural Network Playground” http://www.ccom.ucsd.edu/~cdeotte/programs/neuralnetwork.html
- Daniel Shiffman, The Nature of Code, ch.10 https://natureofcode.com/book/chapter-10-neural-networks/
- Daniel Smilkov and Shan Carter, “A Neural Network Playground” https://playground.tensorflow.org/
- The Coding Train, “10: Neural Networks - The Nature of Code” (YouTube playlist) https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aCibgK1PTWWu9by6XFdCfh
Lab: Introduction to TensorFlow
Week 9 - 10/22: Neural networks continued; bias
- Alex Galloway, “Are Algorithms Biased?” http://cultureandcommunication.org/galloway/are-algorithms-biased
- Blaise Agüera y Arcas, Margaret Mitchell and Alexander Todorov, “Physiognomy’s New Clothes” https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a
- Diana ben-Aaron, “Weizenbaum examines computers and society” http://tech.mit.edu/V105/N16/weisen.16n.html
- Julia Angwin et al., “Machine Bias” https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Lab: TensorFlow continued Guest Speaker: TBD
Week 10 - 10/29: Machine learning workflow + project workshop
Due: Lab assignment #3 (submit by 10/28) This will be a lab-focused session. We will learn about data pipelines, re-using your models, etc. Then, we will workshop your project with the main goal of verifying that you are on track with the class timeline.
Lab: Project workshop
Week 11 - 11/5: Machine learning tools; pre-trained models; automation/augmentation
- Patrick Hebron, “Rethinking Design Tools in the Age of Machine Learning” https://medium.com/artists-and-machine-intelligence/rethinking-design-tools-in-the-age-of-machine-learning-369f3f07ab6c
- Shan Carter and Michael Nielsen, “Using Artificial Intelligence to Augment Human Intelligence” https://distill.pub/2017/aia/
- Shannon Mattern, “The Ethics of Automating Design” https://wordsinspace.net/shannon/2019/02/13/the-ethics-of-automating-design/
- Roelof Pieters and Samim Winiger, “Creative AI: On the Democratisation & Escalation of Creativity” https://medium.com/@creativeai/creativeai-9d4b2346faf3
- Wekinator by Rebecca Fiebrink http://www.wekinator.org/
Lab: Introduction to Runway ML Guest Speaker: TBD
Week 12 - 11/12: Machine learning ecosystem; corporate infrastructure
- Abeba Birhane, “The Algorithmic Colonization of Africa” https://reallifemag.com/the-algorithmic-colonization-of-africa/
- Katharine Schwab, “The Dead-Serious Strategy Behind Google’s Silly AI Experiments” https://www.fastcompany.com/90152774/the-dead-serious-strategy-behind-googles-silly-ai-experiments
- Kyle Wiggers, “AI classifies people’s emotions from the way they walk” https://venturebeat.com/2019/07/01/ai-classifies-peoples-emotions-from-the-way-they-walk/
- Wiggers, “AI predicts whether you’ll return an item before you buy it” https://venturebeat.com/2019/07/01/ai-predicts-whether-youll-return-an-item-before-you-buy-it/
- Mark Bergen, “Google Wants to Train Other Companies to Use Its AI Tools” http://web.archive.org/web/20171101212341/https://www.bloomberg.com/news/articles/2017-10-19/google-wants-to-train-other-companies-to-use-its-ai-tools
- M.C. Elish and Tim Hwang, An AI Pattern Language, https://datasociety.net/pubs/ia/AI_Pattern_Language.pdf
- Shana Lynch, “Andrew Ng: Why AI Is the New Electricity” https://www.gsb.stanford.edu/insights/andrew-ng-why-ai-new-electricity
Lab: Runway ML continued
Week 13 - 11/19: Unsupervised learning #1 This will be a lab-focused session that introduces unsupervised learning, which is a different ML paradigm than supervised learning (such as classification and regression).
Lab: Clustering algorithms
Week 14 - 11/26: Unsupervised learning #2
Due: Lab assignment #4 (submit by 11/25) This will be a lab-focused session that continues on the theme of unsupervised learning. Lab: Dimensionality reduction; visualization
Week 15 - 12/3: Labor and machine learning
- Astra Taylor, “The Automation Charade” https://logicmag.io/failure/the-automation-charade/
- Cade Metz, “A.I. Is Learning From Humans. Many Humans.” https://www.nytimes.com/2019/08/16/technology/ai-humans.html
- Kate Crawford and Vladan Joler, “Anatomy of an AI System” https://anatomyof.ai/
- West, S.M., Whittaker, M. and Crawford, K. (2019). Discriminating Systems: Gender, Race and Power in AI. AI Now Institute. https://ainowinstitute.org/discriminatingsystems.pdf
- Shannon Mattern, “Maintenance and Care” https://placesjournal.org/article/maintenance-and-care/
Week 16 - 12/10: Presentations
Due: Final project (before class)
Textbooks, Readings and Materials:
All reading materials and course slides (if applicable) will be provided as hyperlinks or downloadable files through LMS.
Students will need a Google account for certain lab sessions. I believe the Pratt email address can serve this purpose, giving you access to Google Drive and Colab. In the latter part of the course, students will also need an account for Runway ML; details on how to sign up will be provided as needed.
Additional resources including technical tutorials, example projects and datasets, resources about critical discourse, and more are listed on this webpage, and will be updated as necessary: https://www.are.na/achim-koh/machine-learning-fall-2019
Projects, Papers and Assignments:
Readings and Discussions
Throughout the semester, we will survey diverse perspectives about machine learning as a socially situated technology. The assigned readings will be complemented by in-class discussions, typically at the beginning of the class.
Each week (except for weeks with no readings assigned; see course schedule), one or two students will act as motivators and write provocations on the readings of the week on the LMS forum. This will allow us to start the conversation in advance of class and carry it on afterwards. Please post your provocations by the end of the day Sunday before class.
A provocation will include a summary of key points in the readings, as well as questions / observations you would like to raise or make. The provocations will serve as starting points of the in-class discussion and some of them will scaffold towards the project proposal and final project.
Students who are not motivators for the week are expected to complete the readings before class, and contribute to the discussion in class and/or online by replying to the forum thread.
The latter part of each class will be a lab session related to the topic of the week. Sometimes, the lab session will be accompanied by a lecture-style session before it; in other cases, we will move into the lab session right after discussions.
At the end of some lab sessions, I will give you take-home assignments (4 total). The assignments will scaffold towards the final project. For example, you will be asked to explain some machine learning terminology or write code that does a specific task. Details on how to submit the homework will be communicated in class. The homework assignments are due by the end of the day Monday before the next class.
I may also ask you to write down the amount of time you spent working on the assignment. This amount of time does NOT affect gradings in any way; I am asking in order to gauge whether I am giving you too much work or whether you are having trouble with some of the course material.
I will ask you to choose a topic that you would like to explore in your final project, and to submit a proposal by mid-semester.
On October 1, we will have an in-class activity where you will share your idea(s) and give peer feedback. Your 800- to 1200-word proposal is due by the end of the day Sunday, October 13, and should include:
- A description of the data you intend to use
- A description of the machine learning task you intend to perform
- A tentative and brief survey of existing work on the topic
- A discussion of the significance of your topic
We will discuss the proposal in further detail in the coming weeks.
Final project + presentation
Your final project is to run an experiment that applies a machine learning technique (such as classification, regression, clustering, etc) that we learned on a dataset of your choice. You can design the project as a complete piece on its own, or as a component of a larger project.
Projects are due before the final class in the form of a write-up detailing your work process; you also need to submit the resulting model / dataset and code used. We will dedicate our class on December 10 to presentations.
A detailed rubric for the project and presentation will be distributed separately.
Assessment and Grading
- Lab assignments 20%
- Participation (discussions and peer feedback) 20%
- Project proposal 20%
- Final project 30%
- Presentation 10%
(thumbnail image via xkcd)