10718: Machine Learning in Practice

Previous Versions: Fall 2023 | Fall 2022 | Fall 2021 | Fall 2020 | Spring 2020

Fall 2025: Tues & Thurs, 2:00-3:20 (POS 153)

Important

• All content will be on github in this repo including schedule and detailed syllabus
• All assignments will be on and submitted through canvas
• Class communication and announcements will be primarily through email and piazza

Class Description

This is a project-based course designed to provide students training and experience in solving real-world problems using machine learning, while exploring the interface between research and practice.

The goal of this course is to give students exposure to the nuance of using machine learning in the real-world, where common assumptions (like iid and stationarity) break down, and the growing needs for (and limitations of) approaches that go beyond optimizing for simple model accuracy measures and explore notions of fairness, explainability, robustness, etc. Through project assignments, lectures, discussions, and readings, students will learn about and experience building machine learning systems for real-world problems and data, as well as applying and evaluating the utility of proposed methods for enhancing the interpretability and fairness of machine learning models. Through the course, students will develop skills in problem formulation, working with messy (aka real) data, making ML design choices appropriate for the problem at hand, model selection, model interpretability, understanding and mitigating bias & disparities, and evaluating the impact of deployed models.

Course Learning Objectives

• Learn how to design end-to-end ML systems that tackle real-world problems
• Evaluate the impact of various design choices across the machine learning workflow in the context of real-world problems.
• Take real-world questions involving data and evaluate or develop appropriate methods to answer these questions.
• Present technical material clearly, in spoken and written form, to various audiences

People

Instructor

Rayid Ghani
GHC 8023 Office Hours: Tuesday 12:30-1:30pm Wednesday 3-4pm Email me if you want to meet outside these hours

Education Associate

Daniel Bird
Office: GHC 8120

Teaching Assistants

Chancharik Mitra	Namrata Deka	Rohan Venkatesh Kashyap
Office Hours: Tues 5pm and Thurs 11am GHC 8228	Office Hours: Mon 4pm and Fri 10am GHC 8228	Office Hours: Tues 12:30pm and Wed 11am GHC 8228

Grading

Project update assignments (30%)

Midterm take-home exam (20%)

Write-up on module 2 findings (10%)

Group presentation (10%)

Final reflection write-up (10%)

Class attendance and participation in discussions (15%)

Submitting weekly check-in and feedback forms (5%)

Schedule

See the detailed syllabus below for much more detail as well, including links to required readings and information about group projects, grading, and helpful optional readings.

Week	Dates	Topic	Assignments
1	Tu: Aug 26	Class Intro and Overview
1	Th: Aug 28	Why ML systems can fail in practice
2	Tu: Sep 2	Scoping and Defining ML Projects	Individual Assignment: Getting to know the class project (due tuesday) Project Team Selection
2	Th: Sep 4	Getting, Storing, and Linking Data
3	Tu: Sep 9	Data Exploration	Assignment on Data Exploration (Due) and Team Pesentations on Data Exploration
3	Th: Sep 11	Analytical Formulation / Baselines
4	Tu: Sep 16	Model Selection Methodology	Project Assignment 1: Formulation and Baseline (due Monday)
4	Th: Sep 18	Performance Metrics
5	Tu: Sep 23	Feature Engineering and Imputation	Project Assignment 2: Validation set up Initial pipeline with train and validation set(s) and baseline implemented (due Monday)
5	Th: Sep 25	ML Pipelines
6	Tu: Sep 31	Models/hyperparameters in practice	Project Assignment 3: list of features and some subset implemented (due Monday)
6	Th: Oct 2	Temporal Model Selection
7	Tu: Oct 7	Module 1 Review: Applied ML - End to End Pipelines	Project Assignment 4: modeling results (due Monday)
7	Th: Oct 9	no class for midterm time	Take-Home Midterm Available
8	Tu: Oct 14	No Class - Mid-semester break
8	Th: Oct 16	No Class - Mid-semester break
9	Tu: Oct 21	Common ML Pitfalls	Add assignment: importances and cross tabs
9	Th: Oct 23	ML Ethics Issues Overview	Updated model results assignment (+ model selection) Due Monday
10	Tu: Oct 28	Understanding the Models	importances + cross tabs assignment due
10	Th: Oct 30	Interpretability
11	Tu: Nov 4	No class - Election Day
11	Th: Nov 6	Fairness in ML
12	Tu: Nov 11	Fairness in ML
12	Th: Nov 13	ML and Causal Inference
13	Tu: Nov 18	Evaluating ML Systems in the Field
13	Th: Nov 20	ML Ops
14	Tu: Nov 25	Uncertainlty Quantificaion
14	Th: Nov 27	Thanksgiving holiday
15	Tu: Dec 2	TBD	Writeup Due
15	Th: Dec 4	Wrap-Up
	Finals Week		Final Reflection Writeup Due

Week	Dates	Topic	Assignments
1	Tu: Aug 26	Class Intro and Overview
1	Th: Aug 28	Why ML systems can fail in practice
2	Tu: Sep 2	Scoping and Defining ML Projects	Individual Assignment: Getting to know the class project (due tuesday) Project Team Selection
2	Th: Sep 4	Getting, Storing, and Linking Data
3	Tu: Sep 9	Data Exploration	Short Assignment on Data Exploration
3	Th: Sep 11	Analytical Formulation / Baselines
4	Tu: Sep 16	Model Selection Methodology	Project Assignment 1: Formulation and Baseline (due Monday)
4	Th: Sep 18	Performance Metrics
5	Tu: Sep 23	Feature Engineering and Imputation	Project Assignment 2: Validation set up Initial pipeline with train and validation set(s) and baseline implemented (due Monday)
5	Th: Sep 25	ML Pipelines
6	Tu: Sep 30	Models/hyperparameters in practice	Project Assignment 3: list of features and some subset implemented (due Monday)
6	Th: Oct 2	Temporal Model Selection
7	Tu: Oct 7	Module 1 Review: Applied ML - End to End Pipelines	Project Assignment 4: modeling results (due Monday)
7	Th: Oct 9	no class for midterm time	Take-Home Midterm Available
8	Tu: Oct 14	No Class - Mid-semester break
8	Th: Oct 16	No Class - Mid-semester break
9	Tu: Oct 21	Common ML Pitfalls	Add assignment: importances and cross tabs
9	Th: Oct 23	ML Ethics Issues Overview	Updated model results assignment (+ model selection) Due Monday
10	Tu: Oct 28	Understanding the Models	importances + cross tabs assignment due
10	Th: Oct 30	Interpretability
11	Tu: Nov 4	No class - Election Day
11	Th: Nov 6	Fairness in ML
12	Tu: Nov 11	Fairness in ML
12	Th: Nov 13	ML and Causal Inference
13	Tu: Nov 18	Evaluating ML Systems in the Field
13	Th: Nov 20	ML Ops
14	Tu: Nov 25	Uncertainlty Quantificaion
14	Th: Nov 27	Thanksgiving holiday
15	Tu: Dec 2	TBD	Writeup Due
15	Th: Dec 4	Wrap-Up
	Finals Week		Final Reflection Writeup Due

Projects and Deliverables

Broadly, the course will be divided into two modules: 1) applied end-to-end machine learning pipelines, 2) Key considerations when building ML systems in practice, such as interpretability, fairness, uncertainty quantification, privacy, MLOps. Throughout the course, students will work in groups of 4 on an applied project based on a real-world problem to explore the ideas and methods covered in each module in detail. During the project, students will be responsible for several key deliverables:

• Throughout the first module (covering applied ML pipelines), groups will submit short project update assignments on a weekly basis to get feedback on their approach and initial results.

• At the end of the first module, there will be a take-home midterm exam focused on the concepts and skills emphasized in this portion of the course.

• During the second half, each group will pick one topic (among a few choices) and implement that into their project and present their results (through a short writeup and a team presentation).

More details about the class project

Public schools in the United States face large disparities in funding, often resulting in teachers and staff members filling these gaps by purchasing classroom supplies out of their own pockets. DonorsChoose is an online crowdfunding platform that tries to help alleviate this financial burden on teachers by allowing them to seek funding for projects and resources from the community (projects can include classroom basics like books and markers, larger items like lab equipment or musical instruments, specific experiences like field trips or guest speakers).

Projects on DonorsChoose expire after 4 months, and if the target funding level isn't reached, the project receives no funding. Since its launch in 2000, the platform has helped fund over 2 million projects at schools across the US, but about 1/3 of the projects that are posted nevertheless fail to meet their goal and go unfunded.

The Modeling Problem

For the purposes of the class project, \DonorsChoose has hired a digital content expert who will review projects and help teachers improve their postings and increase their chances of reaching their funding threshold. Because this individualized review is a labor-intensive process, the digital content expert has ** time to review and support only 10% of the projects posted to the platform on a given day**.

You are working with DonorsChoose, and your task is to help this content expert focus their limited resources on projects that most need the help. As such, you want to build a model to identify projects that are least likely to be fully funded before they expire and pass them off to the digital content expert for review.

Data

Download links and data set description

Grace Days

Project teams receive 3 total grace days for use on your project deliverables. You may not use more than 1 grace day on any single assignment. We will automatically keep a tally of these grace days for you; they will be applied greedily.

Participation and Missing Days

Attendance in class and participation in class discussions is a large part of 10-718. Throughout the semester your participation will be measured by your responses in class and via Slido. You are permitted to miss a maximum of 4 lectures in order to still be considered for full participation credit, more than this will begin to reduce your participation grade.

Structure

Below is a preliminary schedule of the course, including the readings that will be assigned for that week. Please be sure to have read and be prepared to discuss the readings before the specified class session. Most of these topics can be (and often are) the focus of entire courses and generally, we’ll only scratch the surface, but hopefully inspire you to delve deeper into areas that interest you (and you’ll find plenty of open research questions in each). Optional readings are also listed for most sessions which may be of interest to students who wish to delve deeper into a given area as well as provide additional context for your related project work.

MODULE 1: APPLYING ML TO PRACTICAL PROBLEMS**

• Tuesday, August 26: Introduction
  During our initial meeting, we’ll provide an introduction to the class, its goals, and an overview of the applied project we will be using as a motivating example throughout the semester.

• Thursday, August 28: Why ML Systems can Fail in Practice
  In this session, we'll discuss real-world failure modes of ML systems, moving beyond model accuracy to system-level issues including data, deployment, governance, incentives, etc. The goal here is to encourage critical thinking about preventing failures and to motivate the topics to be covered during the rest of the semester.

• Tuesday, September 2:

  ML Project Definition and Scoping

  In this session, we’ll talk about scoping, problem definition, and understanding and balancing organizational goals. Well before we start doing technical ML work, a decision needs to be made about whether a given problem can and should be addressed with machine learning: is the problem significant, feasible to solve with ML, and of sufficient importance to the organization that they will devote resources to implementing the solution? How will success be measured? How will (often competing) goals of efficiency, effectiveness, and equity be balanced?

  Lecture Slides

  Due Today:

  • Individual Assignment: Getting to know the class project data
  • Project team selections

  Required Reading:

  • Data Science Project Scoping Guide Available Online

  Optional Readings:

  • Fine-grained dengue forecasting using telephone triage services by Rehman, NA, et al. Sci. Adv. 2016. Available Online

  • Deconstructing Statistical Questions by Hand, D.J. J. Royal Stat Soc. A 157(3) 1994. Available Online

  • Predictive Modeling for Public Health: Preventing Childhood Lead Poisoning by Potash, E, et al. KDD 2015.

• Thursday, September 4

  Obtaining, Storing, and Linking Data

  In this class, we will look at some of the nuances of obtaining and using data in real-world projects, including a discussion of the strengths and weaknesses of different options for data storage as well as the practical aspects of dealing with linkage of records from many different sources.

  Lecture Slides

  Optional Readings:

  • Data Matching by Christen, P. Springer (2012). Chapter 2: The Data Matching Process Available Online

  • Big Data and Social Science edited by Foster, Ghani, et al. Chapter 4: Databases.Available online

  • Broken Promises of Privacy by Ohm, P. UCLA Law Review. 2009. Introduction and Section 1. Available Online

• Tuesday, September 9

  Data Exploration

  You're all familiar with the concept and practice of data exploration. In this session, we'll learn about the use of data exploration in ML projects and apply those ideas to the class project. Most of this session will be focused on team presentations on the data exploration assignment (see canvas).

• Thursday, September 11

  Analytical Formulation and Baselines

  In this session, we’ll discuss the analytical formulation of applied projects. Distinct from the initial scoping, a true analytical formulation of your problem can only come after you have developed an understanding of the data at hand, which in turn will often result in a greater understanding of the problem itself. Here, you’ll ask how specifically your label (if relevant) is defined in the data, what types of information are available as features, and what baseline you’ll be measuring performance against. Very rarely is the appropriate baseline as simple as “random” or the population prevalence. Rather, it should reflect what would be expected to happen otherwise: perhaps a simple decision rule that an expert would come up with or even a pre-existing statistical model that the current effort is seeking to replace.

  Lecture Slides

  Required Readings:

  • Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations by Obermeyer, Z., Powers, B., et al. Science. 2019. Available Online

  • Problem Formulation and Fairness by Passi and Barocas. FAT* 2019. Available Online

  Optional Readings:

  • Always Start with a Stupid Model, No Exceptions by Ameisen, E. Medium. Available Online

  • Create a Common-Sense Baseline First by Ramakrishnan. Medium. Available Online

  • Data Science for Business by Provost and Fawcett. O’Reilly. 2013. Chapter 2: Business Problems and Data Science Available Online

• DUE MONDAY, SEPTEMBER 15: Project Update 1 (Formulation and Baselines)

• Tuesday, September 16: Model Selection Methodology
  During this session, we’ll begin a discussion of model evaluation with a focus on validation strategies. Introductory machine learning classes tend to focus on techniques such as k-fold cross-validation to guard against over-fitting, but is this always the best approach in practice? How does your choice of validation strategy relate to the manner in which you are hoping your model will generalize?

  Required Reading:

  • Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure by Roberts, DR, Bahn, V, et al. Ecography 40:2017. Available Online

  Optional Readings:

  • Time Series Nested Cross-Validation by Cochrane, C. Medium. Available Online

  • The Secrets of Machine Learning by Rudin, C. and Carlson, D. arXiv preprint: 1906.01998. 2019. Available Online

  • Big Data and Social Science (2nd edition) edited by Foster, Ghani, et al. Section 7.7 of Chapter 7: Machine Learning. Available Online

• Thursday, September 18: Model Performance Metrics
  In this class, we’ll introduce topics around choosing performance metrics and evaluating classifiers. In most cases, a vast array of methods — each with a number of tunable hyperparameters — can be brought to bear on your modeling question. How do you decide which models are better than others and how can you be confident this decision will generalize into the future when the model is deployed? How should you balance considerations of performance, explainability, and fairness when making these decisions? Are models that are performing equally well all learning the same patterns and generating the same predictions? How should you select one to deploy if they are not? In this class, we’ll begin to answer these questions, focusing on the choice of performance metrics and how they relate to your project’s goals, scope, and formulation.

  Required Reading:

  • Transductive Optimization of Top k Precision by Liu, LP, Dietterich, TG, et al. IJCAI 2016. Available Online

• DUE MONDAY, SEPTEMBER 22: Project Update 2 (Validation set-up; initial pipeline with train and validation sets and baseline implemented)

• Tuesday, September 23: Feature Engineering and Imputation
  In many real-world contexts, expressing domain expertise through thoughtful feature engineering can dramatically improve model performance by understanding what underlying factors are likely to be predictive and helping the model find these relationships. Likewise, most data sets you’ll encounter in practice are littered with outliers, inconsistencies, and missingness. Handling these data issues in a smart way can be critical to a project’s success. This class will focus on these aspects of dealing with often messy and inconsistent data encountered in applied projects.

  Optional Readings:

  • Missing Data Conundrum by Akinfaderin, W. Medium. Available Online

  • Feature Engineering for Machine Learning by Zhang, A. and Casari, A. O’Reilly. 2018. Chapter 2: Fancy Tricks with Simple Numbers Available Online

  • Missing-data imputation by Gelman, A. Available Online

• Thursday, September 25: ML Pipelines
  During this session, we’ll take some time to review the machine learning pipelines your teams have been building, providing feedback on structure, design decisions, and best practices.

• DUE MONDAY, SEPTEMBER 29: Project Update 3 (List of implemented and planned features)

• Tuesday, September 30: ML Modeling in Practice
  This class will focus on some of the practical aspects of applying machine learning to real-world problems. In other classes, you have implemented and worked with a wide variety of machine learning methods, but where should you start when dealing with a real problem in practice? What is a “reasonable” hyperparameter grid to consider? What pitfalls might you encounter in these situations and how can you avoid them?

• Thursday, Oct 2: Temporal Model Selection
  During this class, we’ll continue our discussion from the previous week, delving into the details of winnowing down a large number of model specifications to one or a handful that perform “best” for some definition of “best”. In particular, we’ll focus on the common case of machine learning problems with a strong temporal component and the desire to balance performance and stability in model selection.

  Optional Reading:

  • Evaluating and Comparing Classifiers by Stapor, K. CORES 2017. Available Online

• DUE MONDAY, OCTOBER 6: Project Update 4 (Modeling Results)

• Tuesday, October 7: Module 1 Review: Applied ML End-to-End Pipelines
  In this session, we’ll take some time to step back and review the concepts we have covered so far, with the goals of helping ensure all the projects are on track for the second and third modules, preparing for next week’s concept-focused midterm exam, and highlighting what we see as the most important takeaways from this section of the course.

  Required Readings:

  • Three Pitfalls to Avoid in Machine Learning by Riley, P. Nature. 527. 2019 (Comment) Available Online

  • Top 10 ways your Machine Learning models may have leakage by Ghani, R. et al. DSSG Blog. Available Online

• Thursday, October 9: No Class, Midterm Distributed
  We'll cancel class on this day to provide some extra time for working on the midterm and updating model results.

• Tuesday, October 14 and Thursday, October 16: NO CLASSES – Fall Break

• DUE TUESDAY, OCTOBER 21: Take-home midterm exam

• DUE MONDAY, OCTOBER 21: Project Update 5 (Updated Modeling Results and Model Selection)

MODULE 2: Key Considerations Beyond Model Accuracy**

This module will focus on topics such as ethics, interpretability, fairness, robustness, privacy, causality, field trials, uncertainty quantification, and supporting decision-makers. The topics to be covered will be chosen as we go through the semester collaboratively.

More Resources

You may find a number of books useful as general background reading on specific topics covered in class, but these are by no means required texts for the course:

• Big Data and Social Science edited by Foster, Ghani, et al. Available Online

• Practical Fairness: Achieving Fair and Secure Data Models by Nielsen

• Fairness and Machine Learning by Barocas, Hardt, and Narayana

• Weapons of Math Destruction by O’Neil

• Exploratory Data Analysis by Tukey

• Data Science for Business by Provost and Fawcett

Additionally, the Global Communication Center (GCC) can provide assistance with the written or oral communication assignments in this class. The GCC is a free service, open to all students, and located in Hunt Library. You can learn more on the GCC website: cmu.edu/gcc.

Your Responsibilities

Attendance: Because much of this course is focused on discussion with your classmates, attending each session is important to both your ability to learn from the course and to contribute to what others get out of it as well. As such, you’ll be expected to attend every session and your participation will factor into your grade as described above. Should anything come up that will require you to miss a class (illness, conferences, etc), please let one of the course staff know in advance.

Academic Integrity: Violations of class and university academic integrity policies will not be tolerated. Any instances of copying, cheating, plagiarism, or other academic integrity violations will be reported to your advisor and the dean of students in addition to resulting in an immediate failure of the course.

AI Use Policy: We want this class to reflect what solving problems with ML in the real world looks like, which means different policies depending on 1) where you're working 2) the data you’re using, and 3) the privacy and confidentiality requirements. For the data we are using in this class, as long as you don't share or upload any confidential information to any AI tool (on the web), you can use any tool you want. You're accountable for the output and the work you submit. Know that a lot of these models are trained on pretty bad ML code and practices :)

We also want this class to help you understand what the AI tools are good for, where they fall short, and how to best use them to solve real-world problems. So use them, but be skeptical, review and test the output, and be ready to share what you find with others in the class.

tl;dr

• You can use anything you want, but you're accountable and responsible for what you submit
• Be prepared to disclose and share what you used and how you used it so others can learn

Resources

Students with Disabilities: We value inclusion and will work to ensure that all students have the resources they need to fully participate in our course. Please use the Office of Disability Resource’s online system to notify us of any necessary accommodations as early in the semester as possible. If you suspect that you have a disability but are not yet registered with the Office of Disability Resources, you can contact them at access@andrew.cmu.edu

Health and Wellness: As a student, you may experience a range of challenges that can interfere with learning, such as strained relationships, increased anxiety, substance use, feeling down, difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events may diminish your academic performance and/or reduce your ability to participate in daily activities. CMU services are available, and treatment does work.
All of us benefit from support during times of struggle. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is almost always helpful.

If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.

If you or someone you know is feeling suicidal or in danger of self-harm, call someone immediately, day or night:
CaPS: 412-268-2922
Re:solve Crisis Network: 888-796-8226

If the situation is life threatening, call the police
On campus: CMU Police: 412-268-2323
Off campus: 911

Discrimination and Harassment: Everyone has a right to feel safe and respected on campus. If you or someone you know has been impacted by sexual harassment, assault, or discrimination, resources are available to help. You can make a report by contacting the University’s Office of Title IX Initiatives by email (tix@andrew.cmu.edu) or phone (412-268-7125).

Confidential reporting services are available through the Counseling and Psychological Services and University Health Center, as well as the Ethics Reporting Hotline at 877-700-7050 or www.reportit.net (user name: tartans; password: plaid).
You can learn more about these options, policies, and resources by visiting the University’s Title IX Office webpage at https://www.cmu.edu/title-ix/index.html
In case of an emergency, contact University Police 412-268-2323 on campus or call 911 off campus.

Student Academic Success Center (SASC)

SASC focuses on creating spaces for students to engage in their coursework and approach learning through a variety of group and individual tutoring options. They offer many opportunities for students to deepen their understanding of who they are as learners, communicators, and scholars. Their workshops are free to the CMU community and meet the needs of all disciplines and levels of study. SASC programs to support student learning include the following (program titles link to webpages):

• Academic Coaching – This program provides holistic, one-on-one peer support and group workshops to help undergraduate and graduate students implement habits for success. Academic Coaching assists students with time management, productive learning and study habits, organization, stress management, and other skills. Request an initial consultation here.

• Peer Tutoring – Peer Tutoring is offered in two formats for students seeking support related to their coursework. Drop-In tutoring targets our highest demand courses through regularly scheduled open tutoring sessions during the fall and spring semesters. Tutoring by appointment consists of ongoing individualized and small group sessions.You can utilize tutoring to discuss course related content, clarify and ask questions, and work through practice problems. Visit the webpage to see courses currently being supported by Peer Tutoring.

• Communication Support – Communication Support offers free one-on-one communication consulting as well as group workshops to support strong written, oral, and visual communication in texts including IMRaD and thesis-driven essays, data-driven reports, oral presentations, posters and visual design, advanced research, application materials, grant proposals, business and public policy documents, data visualisation, and team projects. Appointments are available to undergraduate and graduate students from any discipline at CMU. Schedule an appointment on their website (in-person, zoom synchronous, or recorded video), attend a workshop, or consult handouts or videos to strengthen communication skills.

• Language and Cross-Cultural Support – This program supports students seeking help with language and cross-cultural skills for academic and professional success through individual and group sessions. Students can get assistance with writing academic emails, learning expectations and strategies for clear academic writing, pronunciation, grammar, fluency, and more. Make an appointment with a Language Development Specialist to get individualized coaching.

• Supplemental Instruction (SI) – This program offers a non-remedial approach to learning in historically difficult courses at CMU. It utilizes a peer-led collaborative group study approach to help students succeed and is facilitated by an SI leader, a CMU student who has successfully completed the course. SI offers a way to connect with other students studying the same course, a guaranteed weekly study time that reinforces learning and retention of information, as well as a place to learn and integrate study tools and exam techniques specific to a course. Visit the website to see courses with SI available here.