Lecture Slides

A complete lecture course for machine learning systems, ready to teach as-is or adapt to your own syllabus.

What a lecture actually looks like. Samples from Volume I and Volume II.

Download All (PDF + PPTX) Teaching Guide View Source
Slide Decks
Volume I

Introduction to Machine Learning Systems

The full single-machine ML stack: data engineering, neural computation, architectures, frameworks, training, compression, hardware, serving, and operations.

17 lectures
 Volume II

Machine Learning Systems at Scale

Distributed infrastructure: compute clusters, network fabrics, distributed training, fault tolerance, fleet orchestration, inference at scale, and governance.

18 lectures
 TinyML

Tiny Machine Learning

The edX courseware: ML fundamentals, keyword spotting, visual wake words, anomaly detection, embedded deployment on Arduino, and MLOps at scale.

4 courses
What You Get
Semester plans 16-week schedules for Volume I and Volume II, a combined 32-week sequence, and a 10 to 12 week TinyML syllabus. Plans →
Speaker notes Every slide carries timing, teaching guidance, the errors students actually make, and discussion prompts. Guide →
In-class exercises At least five per lecture, with worked answers. Prediction, calculation, peer instruction, and retrieval practice. Details →
Ready to present PDF for projection and PPTX for presenter mode and annotation. Nothing to install. Download →
Yours to edit Full Beamer source with the theme, and every diagram as editable vector art rather than a flattened image. Customize →
License CC BY-NC-SA 4.0. Teach from it, adapt it, and share your version, with attribution. Terms →
Related Resources
Textbook The slides are derived from this two-volume open textbook. Read online or download PDF. Vol I · Vol II
Instructor Blueprint Syllabi, assessment rubrics, TA guide, and course customization — the full teaching toolkit. Blueprint →
Interactive Labs 34 browser-based labs powered by MLSys·im. Pair with slides for hands-on learning. Labs →
Hardware Kits Arduino, Raspberry Pi, and Seeed deployment labs for TinyML and edge AI. Kits →

Teach from the decks

Use the teaching guide for semester plans, customization notes, active-learning prompts, and source-level rebuild instructions.

Teaching Guide GitHub

Part of the MLSysBook Ecosystem

Textbook

Comprehensive theory across the full ML systems stack.

Lecture Slides

Beamer decks and teaching materials for every chapter.

Labs

Interactive Marimo notebooks that measure the book's claims.

Hardware Kits

Hands-on embedded ML deployment on real devices.

Instructor Hub

Course maps, syllabi, and adoption resources for teaching.

TinyTorch

Build your own ML framework from scratch, module by module.

MLSys·im

The analytical modeling engine behind the book's quantitative figures.

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