10718: Machine Learning in Practice

Previous Versions: Fall 2022

Fall 2021

Fall 2020

Spring 2020

Fall 2023: Tues & Thurs, 3:30-4:50 (HOA 160)

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 Slack

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 with a particular focus on topics in fairness and explainability.

The goal of this course is to give students exposure to the nuance of applying machine learning to the real-world, where common assumptions (like iid and stationarity) break down, and the growing needs for (and limitations of) approaches to improve fairness and explainability of these applications. 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.

People

Instructor

Rayid Ghani
GHC 8023 Office Hours: Tue 2-3, Thu 1-2

Teaching Assistant

Catalina Vajiac
	Office Hours: Monday 2-3pm, Thursday 2-3pm GHC 8018

Grading

Note that this course is being offered pass/fail. Each time you ask how each action you take will affect your grade will result in lowering of the grade :)

Weekly project update assignments (10%)

Midterm take-home exam (20%)

Write-up on interpretability findings (15%)

Write-up on fairness findings (15%)

Group presentation (5%)

Final reflection write-up (15%)

Quizzes on readings and concepts (5%)

Class attendance and participation (10%)

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 29	Class Intro and Overview
1	Th: Aug 31	ML Project Scoping	Project Team Selection
2	Tu: Sep 5	Getting, Storing, and Linking Data	Individual Assignment: Getting to know the class project (due Monday)
2	Th: Sep 7	Analytical Formulation / Baselines
3	Tu: Sep 12	Model Selection Methodology	Project Assignment 1: Formulation and Baseline (due Monday)
3	Th: Sep 14	Performance Metrics
4	Tu: Sep 19	Feature Engineering and Imputation	Project Assignment 2: Validation set up Initial pipeline with train and validation set(s) and baseline implemented (due Monday)
4	Th: Sep 21	ML Pipelines
5	Tu: Sep 26	Models/hyperparameters in practice	Project Assignment 3: list of features and some subset implemented (due Monday)
5	Th: Sep 28	Temporal Model Selection
6	Tu: Oct 3	Review of modeling results	Project Assignment 4: modeling results (due Monday)
6	Th: Oct 5	Common ML Pitfall in Practice
7	Tu: Oct 10	Module 1 Review: Applied ML - End to End Pipelines	Updated model results assignment (+ model selection) Due Monday
7	Th: Oct 12	no class for midterm time	Take-Home Midterm Available
8	Tu: Oct 17	No Class - Mid-semester break
8	Th: Oct 19	No Class - Mid-semester break
9	Tu: Oct 24	ML Ethics Issues Overview	Add assignment: importances and cross tabs
9	Th: Oct 26	Interpretability: Intro and Overview, taxonomy
10	Tu: Oct 31	Understanding the Models	importances + cross tabs assignment due
10	Th: Nov 2	Interpretability Methods: Inherently Interpretable (GA2Ms, RiskSLIM, etc.)
11	Tu: Nov 7	Interpretability Methods:: Post-Hoc Local/Feature-based (LIME, SHAP
11	Th: Nov 9	Interpretability Methods: Other methods (counterfactual DICE, example-based ProtoDash, etc.)	Interpretability Writeup Due on Friday
12	Tu: Nov 14	Fairness in ML Overview
12	Th:Nov 16	Field Trials and Causality
13	Tu: Nov 21	Fairness Methods: Pre-processing (removing sensitive attribute, sampling)
13	Th: Thanksgiving	Thanksgiving holiday
14	Tu: Nov 28	Fairness Methods: In-processing (Zafar, Celis, fairlearn, etc.)
14	Th: Nov 30	Fairness Methods: Post-Processing (Hardt, LA, etc)
15	Tu: Dec 5	Module 3 Review: ML Fairness
15	Th: Dec 7	Wrap-Up	Bias Writeup Due on Friday
	Finals Week		Final Reflection Writeup Due (Date TBD)

Projects and Deliverables

Broadly, the course will be divided into three modules: 1) applied end-to-end machine learning pipelines, 2) model interpretability, and 3) fairness in machine learning. Throughout the course, students will work in groups of 4-5 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 interpretability and fairness modules, each group will be responsible for a 15-minute presentation on one of the methods we will consider in class. This presentation should introduce the method at a high level, provide a general understanding of how it works, discuss preliminary results from implementing the method for their project, and give some thought to its strengths and weaknesses. A second group will be assigned for each method as a “discussant” both of these groups will need to implement the method before class.

• Also during these modules, groups will each be responsible for applying three of the methods we discuss in the context of their project (one from each day of discussion; note that you may use either an existing implementation or write your own). At the end of the module, the analysis and results obtained with these methods should be written up in the format of an extended technical abstract 3-4 pages in length, detailing the methods, how they were used in their project, findings, and any recommendations.

Structure

The course is divided into three modules:

1. Applying ML to Practical Problems

2. Understanding ML Models

3. Fairness in ML

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 29: 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 31: Project Scoping
  DUE TODAY: Project team selections

  In this session, we’ll talk about scoping, problem definition, and understanding and balancing organizational goals. Well before the outset of technical 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 a technical approach, 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? During this lecture, we’ll also touch on topics surrounding acquiring data and record linkage across systems.

  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.

• DUE TUESDAY, SEPTEMBER 5: Individual Assignment: Getting to know the class project data

• Tuesday, September 5: 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.

  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.

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

• Thursday, September 7: 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 choice” 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.

  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*

    1. 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.

    1. Chapter 2: Business Problems and Data Science Available Online

• DUE MONDAY, SEPTEMBER 11: Project Update 1 (Formulation and Baselines)

• Tuesday, September 12: 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 14: 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 18: Project Update 2 (Validation set-up; initial pipeline with train and validation sets and baseline implemented)

• Tuesday, September 19: 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 21: 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 25: Project Update 3 (List of implemented and planned features)

• Tuesday, September 26: 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, September 28: 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 2: Project Update 4 (Modeling Results)

• Tuesday, October 3: Reviewing Project Modeling Results
  In this class, we’ll take some time to look at the modeling results from the group projects, providing some feedback and advice for finalizing the models before applying interpretability methods to them.

• Thursday, October 5: Other ML Problem Templates

• DUE MONDAY, OCTOBER 9: Project Update 5 (Updated Modeling Results and Model Selection)

• Tuesday, October 10: Module I 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 12: 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 17 and Thursday, October 19: NO CLASSES – Fall Break

MODULE 2: UNDERSTANDING ML MODELS**

• DUE TUESDAY, OCTOBER 24: Take-home midterm exam

• Tuesday, October 24: ML Ethics and Fairness Overview
  In this session, we’ll have a discussion about ethical issues in machine learning, focusing on the broader landscape of topics in this area, including questions around privacy, transparency, and accountability. Note that we’ll spend some time in class discussing the case study linked below, so please be sure to have read and thought about it before class.

  Required Readings:

  • Princeton Ethics Case Study 6: Public Sector Data Analysis Available Online

  Optional Readings:

  • Ethics and Data Science by Loukides, M., Mason, H., and Patil, D.J. O’Reilly (2018). Entire Book (don’t worry – it’s short!) Available Online (When prompted to select institution, select ’Not listed? Click here’ and enter your CMU email address to access content)

• Thursday, October 26: Model Interpretability Overview
  Model interpretability can be thought of at two levels: global (how the model works in aggregate) and local (why an individual prediction came out as it did). In this class, we’ll focus on the bigger picture: discussing the landscape of model interpretability as well as different use cases and users.

  Note that we’ll also post assignments for the Module II methods early this week so teams can begin implementing them for the deep dives beginning the following week.

  Required Readings:

  • Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions by Amarasinghe, K., et al. arXiv preprint: arxiv/2010.14374 Available Online

  • Benchmarking and Survey of Explanation Methods for Black Box Models by Bodria, F., et al. arXiv preprint: arxiv/2102.13076 Available Online

• DUE TUESDAY, OCTOBER 30: Project Update 6 (Understanding Your Models)

• Tuesday, October 30: Practical Understanding of ML Models
  During this session, we’ll be introducing some simple and practical analyses to perform after the modeling process and what it means to compare performance across model specifications. These methods can help provide a basic understanding of how your model is distinguishing between predicted classes and play an important role in detecting bugs such as leakage.

• Thursday, November 2: Inherently-Interpretable Methods
  In this class, we’ll start our deeper dives into specific methods by looking at inherently interpretable methods, including GA²M models and RiskSLIM. Groups will give a 15-minute presentation on each method and then we will spend the remainder of the class session comparing the methods and discussing the use cases in which they may be most appropriate.

  Required Readings to Skim:

  • Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission by Caruana, R, et al. KDD 2015. Available Online

  • Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice by Rudin, C, and Usutn, B. INFORMS Journal on Applied Analytics. 2018. Available Online

  Optional Readings:

  • Interpretable Decision Sets: A Joint Framework for Description and Prediction by Lakkaraju, H, et al. KDD 2016. Available Online

• Tuesday, November 7: Post-Hoc Local Explanations
  In this class, we’ll continue our discussion model interpretability by looking at three methods that can provide local, feature-based explanations without relying on the details of the underlying model: LIME, SHAP, and MAPLE.

  Required Readings to Skim:

  • Why Should I Trust You? Explaining the Predictions of any Classifier by Ribeiro, MT, Singh, S, and Guestrin, C. KDD 2016. Available Online

  • A Unified Approach to Interpreting Model Predictions by Lundberg, SM and Lee, S. NeuRIPS 2017. Available Online

  • Model Agnostic Supervised Local Explanations by Plumb, G, Molitor, D, and Talwalkar, AS. NeuRIPS 2018. Available Online

  Optional Readings:

  • Explainable machine-learning predictions for the prevention of hypoxaemia during surgery by Lundberg, SM, Nair, B, et al. Nature Biomed. Eng. 2018. Available Online

  • Explainable AI for Trees by Lundberg, SM, Erion, G, et al. arXiv preprint: arxiv/1905.04610. Available Online

• Thursday, November 9: Other Interpretability Methods
  In this session, we’ll take a look at some of the other methods people have explored for model interpretability, such as counterfactual (e.g., DiCE) and example-based methods (e.g., ProtoDash)

  Required Readings to Skim:

  • Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations by Mothilal, R, et al. FAT* 2020. Available Online

  • Efficient Data Representation by Selecting Prototypes with Importance Weights by Gurumoorthy, K.S., et al. arXiv preprint: arxiv/1707.01212. Available Online

  Optional Readings:

  • Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable by Tan, S., et al. FODS 2020. Available Online

  • Understanding Black-box Predictions via Influence Functions by Koh, P.W. and Liang, P. ICML 2017. Available Online

• DUE FRIDAY, NOVEMBER 10: Extended Abstract on Interpretability

MODULE 3: FAIRNESS IN ML**

• Tuesday, November 14: Intro to Fairness
  In this class, we’ll introduce topics in ML fairness, where we will focus our methods deep dives for the remainder of the semester: Just as important as assessing whether your model is making accurate predictions is determining whether it is doing so in a fair manner. But, what do we mean by fairness? How can you measure it and what can you do to mitigate any disparities you might find? Where in your pipeline can bias be introduced? (spoiler: everywhere). This class will provide a very brief introduction to the expansive field of algorithmic fairness.

  Note that we’ll also post assignments for the Module III methods for early this week so teams can begin implementing them for the deep dives beginning the following week.

  Required Readings:

  • Fairness Definitions Explained by Verma, S and Rubin, J. Available Online

  • A Theory of Justice by Rawls, J. 1971. Chapter 1: Justice as Fairness, pp. 1-19. Available Online

  • Racial Equity in Algorithmic Criminal Justice by Huq, A. Duke Law Journal. 2018. Available Online [Focus on sections: I.B.2, all of section II, III introduction, III.B, and III.D.3]

  Optional Readings:

  • Is Algorithmic Affirmative Action Legal? by Bent, JR. Georgetown Law Journal. 2019. Available Online

  • Does Mitigating ML’s Impact Disparity Require Treatment Disparity? by Lipton, Z, McAuley, J, and Chouldechova, A. NeuRIPS
    1. Available Online
  • Equality of Opportunity by Roemer, JE and Trannoy, A. 2013. Available Online

• Thursday, November 16: Field Trials and Causality
  In this session, we’ll briefly discuss field trials and issues of causality, critical for understanding how your model actually generalizes to real-world applications. Even with careful planning and handling of the data, the only way to truly understand how well your model works is by testing it in the field. Generally, you’re concerned not only with its predictiveness, but the actual ability of the model to help the program achieve its policy goals, such as improving outcomes among the population it serves. Typically, this involves working closely with policymakers to develop a field trial using either randomization or non-experimental methods depending on the constraints of the setting.

  Required Readings:

  • The seven tools of causal inference, with reflections on machine learning by Pearl, J. Comm ACM. 2019 Available Online

  Optional Readings:

  • Elements of Causal Inference by Peters et al. MIT Press. Chapters 1 and 2. Available Online (Open Access Link)

• Tuesday, November 21: Pre-Processing FairML Methods
  During this class, we’ll start exploring fairness-enhancing methods by specifically looking at pre-processing methods that work upstream of the machine learning models themselves in hopes of reducing downstream biases in the pipeline. In particular, we will focus on two strategies: removing sensitive attributes (and correlated features) and re-sampling the training data. Note for this week that the presenting groups should focus on these strategies generally, rather than one specific implementation in the readings here (which are provided as examples of these approaches).

  Required Readings to Skim:

  • Interventional Fairness: Causal Database Repair for Algorithmic Fairness by Salimi, B., et al. SIGMOD 2019. Available Online

  • Data preprocessing techniques for classification without discrimination by Kamiran, F. and Calders, T. Knowledge Information Systems (2012). Available Online

  • Tuning Fairness by Balancing Target Labels by Kehrenberg, T., et al. Frontiers in Artificial Intelligence (2020). Available Online

  Optional Readings:

  • Fairness Through Awareness by Dwork, C, Hardt, M, et al. ITCS
    1. Available Online

• Thursday, November 23: NO CLASSES – Thanksgiving

• Tuesday, November 28: In-Processing FairML Methods
  In this session, we’ll look at in-processing methods for improving fairness by adding constraints (or regularization terms) to the optimization performed during model training, focusing on methods proposed by Zafar, Celis, and Microsoft Research.

  Required Readings to Skim:

  • Fairness Constraints: Mechanisms for Fair Classification Zafar, M, Valera I, et al. PMLR 2017. Available Online

  • Classification with fairness constraints: A meta-algorithm with provable guarantees by Celis, E, Huang, L, et al. FAT* 2019. Available Online

  • Fairlearn: A toolkit for assessing and improving fairness in AI by Bird, S., et al. Microsoft Research Whitepaper (2020). Available Online

  Optional Readings:

  • Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment by Zafar, M., et al. WWW 2017. Available Online

• Thursday, November 31: Post-Process FairML Methods
  This class, we’ll turn to post-processing methods, including fairness-aware model selection, decoupled models, and post-hoc score adjustments.

  Required Readings to Skim:

  • Promoting Fairness through Hyperparameter Optimization by Cruz, A.F., et al. arXiv preprint: arxiv/2103.12715 Available Online

  • Decoupled Classifiers for Group-Fair and Efficient Machine Learning by Dwork, C, et al. PMLR 2018. Available Online

  • Equality of Opportunity in Supervised Learning by Hardt, M. and Price, E. NeurIPS 2016. Available Online

  Optional Readings:

  • Case study: predictive fairness to reduce misdemeanor recidivism through social service interventions by Rodolfa, K.T., et al. FAT* 2020. Available Online

• Tuesday, December 5: Module III Review
  In this session, we’ll wrap up our discussion of ML interpretability with a brief review of the methods we’ve covered (and what we haven’t had time to discuss)

• Thursday, December 7: Wrap-Up
  For this meeting, we’ll wrap up the course with a review of the topics we’ve covered throughout the semester and some comments on the important take-home messages we hope will be useful in your future research and work.

• DUE FRIDAY, DECEMBER 8: Extended Abstract on Fairness

• DUE DATE TBD (Finals Week): Final Reflection Write-Up
  As a final assignment, we’re asking each student to write up a brief (1-2 page) reflection on the course. A more detailed assignment and prompt will be posted to canvas.

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 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.

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.