Machine Learning for Computer Systems
This course will cover topics at the intersection of machine learning and systems, with a focus on applications of machine learning to computer systems. Topics covered will include applications of machine learning models to security, performance analysis, and prediction problems in systems; data preparation, feature selection, and feature extraction; design, development, and evaluation of machine learning models and pipelines; fairness, interpretability, and explainability of machine learning models; and testing and debugging of machine learning models.
Scope and focus. This is a practical, hands-on introduction to the machine-learning workflow — data, features, models, evaluation, and deployment — taught through applications to computer systems, and primarily through computer networking, which is the instructor’s area of expertise and the domain of the companion textbook, Machine Learning for Networking. The ML concepts are general and transfer to any systems domain, but expect the examples, datasets, and assignments to be networking-centric. If you are looking for a theory- or math-first ML course, or one that ranges broadly across operating systems, databases, and architecture, this is not that course.
You can and should think of this course as a practical hands-on introduction to machine learning models and concepts that will allow you to apply these models in practice. We’ll focus on examples from networking, but you will walk away from the course with a good understanding of how to apply machine learning models to real-world datasets, how to use machine learning to help computer systems operate better, and the practical challenges with deploying machine learning models in practice.
More details are in the course syllabus.
Class agenda for each meeting is in the agenda (one file per term; the current term is Autumn 2026).
Current term (Autumn 2026, Chicago): the standard nine-week course as described in the syllabus: four assignments, a midterm, a final, and a group project. Dates are in the pinned sheet in Slack. Past terms with their own rules: Summer 2026 (Paris).
| Lecture | Topic | Reading |
|---|---|---|
| 1 | Introduction (Packet Capture) |
Ch. 1 |
| Use Cases | ||
| 2 | Security (Scanning) |
Ch. 2.1 |
| 3 | Performance (QoE Inference) |
Ch. 2.2 |
| 4 | Resource Optimization | Ch. 2.3 |
| Data From Computer Systems | ||
| 5 | Data Acquisition (Data Acquisition) |
Ch. 3.2–3.3 |
| 6 | From Data to Analysis (Feature Extraction) |
Ch. 3.4 |
| Machine Learning Pipeline | ||
| 7 | Data Preparation and Representation (Data Preparation) |
Ch. 4.1 |
| 8 | Model Training and Evaluation (Model Evaluation) |
Ch. 4.2–4.3 |
| Supervised Learning | Ch. 5 | |
| 9 | Non-Parametric and Probabilistic Models (Naive Bayes) |
|
| 10 | Linear and Polynomial Regression (Linear Regression) |
|
| 11 | Logistic Regression and SVMs (Logistic Regression) |
|
| 12 | Trees and Ensembles (Trees and Ensembles) |
|
| 13 | Deep Learning (Deep Learning) |
|
| 14 | Learning Traffic Representations (nPrint) |
|
| Unsupervised Learning | Ch. 6 | |
| 15 | Dimensionality Reduction (Dimensionality Reduction) |
|
| 16 | Clustering (Clustering) |
|
| Generative Models | Ch. 7 | |
| 17 | Diffusion Models (Diffusion Models) |
|
| 18 | State-Space Models (NetSSM walkthrough · open in Colab, needs a GPU) |
|
| 19 | Transformers (Large Language Models) |
|
| Bonus Topics | Ch. 8 | |
| 20 | Timeseries Analysis (Timeseries) |
|
| 21 | Reinforcement Learning (Reinforcement Learning) |
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| 22 | Autoencoders (covered within Lecture 15) | |
| 23 | Model Performance and Maintenance (slides · speaker notes) |
Please come to class having done the reading.
The nine-week course has a midterm and a final; short terms have one combined exam (see the term page). The permitted-materials policy is the same for every exam and is printed on it: closed-book and closed-notes except for one 8.5×11-inch sheet of paper with handwritten notes (both sides permitted); no electronic devices or other materials. Details in exams.
Past exams from prior years are available to everyone as practice material:
past midterms and past finals. You can also generate your
own practice exams from the agenda with the prompts in prompts/.
The material below is strictly optional unless otherwise noted, although you may find it useful.