Paris: Machine Learning for Networked Systems

September 2026

Course Title: CMSC 25422 Machine Learning for Computer Systems
(equivalent course: DATA 25422)

Instructor: Nick Feamster, Neubauer Professor, Department of Computer Science

This intensive three-week course explores the intersection of machine learning and networked systems, examining how AI techniques can be applied to optimize, secure, and understand complex network infrastructures. The course will combine theoretical foundations with hands-on experiential learning through site visits to leading French research institutions.

Paris is an ideal location for this program as it hosts numerous world-class research institutions working on AI and networked systems, including INRIA (French National Institute for Research in Digital Science and Technology), École Normale Supérieure (ENS) de Lyon, major telecommunications companies, and a thriving tech ecosystem. The proximity to European research networks and the existing UChicago IIRP infrastructure, including the GATEAU (Generative AI Techniques for Network Management) collaboration, will provide students with unique access to cutting-edge research and industry applications in networked AI systems.

Based on the established Machine Learning (ML) Systems curriculum, students will:

• Apply machine learning models to computer systems problems, with focus on networking applications
• Master data preparation, feature selection, and feature extraction for systems data
• Design, develop, and evaluate machine learning models and pipelines for network performance analysis
• Understand fairness, interpretability, and explainability challenges in ML models for systems
• Gain hands-on experience with testing and debugging of machine learning models in networked environments
• Develop practical skills in applying machine learning to real-world network datasets
• Learn to use machine learning to help computer systems operate more efficiently
• Understand practical challenges of deploying machine learning models in production network environments

Site visits and experiential learning activities may include:

• ENS Lyon Computer Science Department: Direct collaboration with Francesco Bronzino and his team working on networked systems and performance evaluation, building on existing research partnerships
• INRIA research laboratories: Focus on networking, AI, and systems research, particularly teams working on network optimization and ML applications
• NetMicroscope offices: Visit to the startup co-founded by Nick Feamster and Francesco Bronzino, demonstrating real-world application of ML for network monitoring and performance inference
• Orange Labs: Major French telecom research center for telecommunications and networking innovation
• French National Cybersecurity Agency (ANSSI): Applications of ML in network security and anomaly detection
• Station F startup ecosystem: Engage with Paris-based startups working on networking, AI, and systems optimization
• European telecommunications infrastructure sites: Hands-on exposure to large-scale network operations

Collaborative research components may include:

• Joint research sessions with GATEAU project partners exploring generative AI applications for network management
• Workshops with ENS Lyon researchers on network performance measurement and optimization
• Hands-on projects analyzing real network data from European infrastructure
• Implementation of ML algorithms for network optimization using datasets from partner institutions
• Collaborative projects with European students and researchers

This program offers a distinctive opportunity for UChicago students to engage with European approaches to networked AI systems, access research facilities and partnerships not available in the US (particularly through ENS Lyon and GATEAU collaborations), and build international connections in the rapidly evolving field of AI-driven networking technologies. Given the practical, hands-on nature of the course and its focus on real-world applications, it will be particularly valuable for students considering careers in the tech industry or graduate studies in systems research.

Faculty who teach on this program rotate from year to year. The faculty roster is designated by the program faculty director.

Headquarters for the College’s study abroad programs in Paris is the University of Chicago John W. Boyer Center in Paris, the University’s teaching and research hub in Europe. Since 2003, the Center has been home to a growing array of the College’s hallmark Study Abroad programs and has supported our community of students, faculty, alumni, and partners from around the world. Designed by Studio Gang, the new Center features state-of-the-art classrooms, offices, event and reception spaces, and gathering areas for students, among other features.

Students in the Machine Learning for Networked Systems September program are housed in a residence hall within the Cité Internationale Universitaire (Cité). The Cité, a park-like residential complex in the fourteenth arrondissement, is the international student campus in Paris, though French students also live there. Students reside in single rooms with a private bath and have access to Cité facilities, including a library, theater, laundry, and athletic facilities. Students will have access to common kitchens in the residence halls and can purchase inexpensive meals at the Cité’s restaurant universitaire.

It is important to recognize the cultural context of student housing in France and understand that the amenities of dormitory facilities may vary. Although some of these differences may take some getting used to, remember that cultural differences extend to all aspects of your experience abroad. Having realistic expectations for your term in Paris will help you approach the study abroad experience with a positive attitude.

Participants in the Paris program will take and receive credit for one Computer Science course (100 units): CMSC 25422 Machine Learning for Computer Systems (equivalent course: DATA 25422). This course is considered part of the students’ Summer Quarter course load and is recorded as a course enrollment on their Summer Quarter registration. The course title, units of credit, and grade are placed on the College transcript.

Completion of a September course abroad will earn students 1 point toward Global Honors, the College’s recognition of exceptional global engagement. Visit the Chicago Language Center’s website for information on how to apply for Global Honors.

Study abroad students pay regular Summer Quarter tuition at the one-course rate, a program fee, and a nonrefundable study abroad administrative fee. The tuition and program fee are paid in conformity with the home campus payment schedule, and a deposit toward the nonrefundable study abroad administrative fee is submitted when accepting a place in a program. Precise figures for the Paris September program during the 2025-2026 year are listed below:

Summer tuition for one course: $4,980 (Summer 2026)

Study abroad administrative fee: $675

Paris September program fee: $4,000

Previous program participants report spending in the range of $200 to $250 per week on meals and incidentals while on the program, though frugal students may spend less, and others could spend much more. Bear in mind that the cost of living in Paris is relatively high and that, while it is possible to live frugally, it is also possible to run short of money if you are unwary. It is therefore essential that you budget your funds prudently, apportioning your resources so that they last for the duration of the program. If you are planning to travel before or after the program or on weekends, you should budget accordingly.

Participants in summer College-sponsored programs are eligible for need-based financial aid, following the procedure described on the Summer and September Aid page of the Financial Aid website. For more information about financial aid resources, please see our general Tuition, Fees, and Funding section.

The prerequisites for the program are CMSC 14300 Systems Programming I or CMSC 15400 Introduction to Computer Systems. Prior to applying for the program, students should have completed introductory computer science courses. They should have experience with basic statistics and probability and preferably some exposure to machine learning concepts (such as CMSC 25300 Mathematical Foundations of Machine Learning, or equivalent). Students should have programming experience in Python and familiarity with data analysis tools. Applications from outside the University are not accepted.

The program is designed for undergraduates in good academic and disciplinary standing who are beyond their first year in the College. While the program stipulates no minimum grade point average, an applicant’s transcript should demonstrate that they are a serious student who will make the most of this opportunity. Because the course is taught in English, there is no language prerequisite.

Each application is examined on the basis of the student’s scholastic record and personal statement. If you are interested in applying for this program, please fill out the online application.

To discuss the Paris: Machine Learning for Networked Systems September program and the possibility of participating, please contact Dana Currier.