cs249r_book/slides/teaching.qmd

---
title: "Teaching Guide"
---

## How to Use These Slides

Each slide deck is designed for **50--75 minutes** of classroom teaching. The decks follow the textbook chapter structure exactly, so students can read the corresponding chapter before or after class.

Use the slides as the classroom layer of the broader curriculum: assign the [textbook](https://mlsysbook.ai) for reading, use [Co-Labs](https://mlsysbook.ai/labs/) for prediction and exploration, use [TinyTorch](https://mlsysbook.ai/tinytorch/) for implementation, use [Hardware Kits](https://mlsysbook.ai/kits/) for deployment, and use [The AI Engineering Blueprint](https://mlsysbook.ai/instructors/) for syllabi, rubrics, and TA workflows.

### Speaker Notes

Every slide has speaker notes containing:

- **Timing guidance** (`[N min]`) for pacing the lecture
- **Teaching tips** for explaining difficult concepts
- **Common student errors** to address proactively
- **Discussion prompts** for engaging the class
- **Transition cues** connecting each slide to the next

To view speaker notes in the PDF, use your PDF reader's presenter mode. In the LaTeX source, notes appear as `\note{...}` commands on each frame.

### Active Learning

Every deck includes **8--11 active learning moments**, distributed across these types:

- **Predict**: Students predict the outcome of a scenario before seeing the answer. Builds intuition about system behavior.
- **Exercise**: A short calculation or analysis task (2--5 minutes). Often involves the Iron Law, roofline analysis, or memory estimation.
- **Discussion**: Open-ended question for pair or group discussion (3--5 minutes). Explores trade-offs and design decisions.
- **Retrieval Practice**: Students recall key concepts from earlier in the lecture or from previous chapters without looking at notes.
- **Muddiest Point**: End-of-lecture check where students identify the concept they found most confusing.

These moments are marked with distinct frame titles and styling so they are easy to find in the deck.

## Customizing the Slides

### Source Format

The slides are **Beamer LaTeX**, not locked PDFs. The full source is open and designed for customization.

```
slides/
  assets/beamerthememlsys.sty   # Theme (colors, fonts, layout)
  vol1/<chapter>/
    <chapter>.tex               # Slide source
    images/                     # SVGs, photos, generated PDFs
  vol2/<chapter>/
    <chapter>.tex
    images/
  Makefile                      # Build system
```

### Building

To build a single deck (PDF):

```bash
cd slides
make ch01            # Builds vol1/01_introduction PDF
```

To build everything (PDF):

```bash
make all
```

To build PowerPoint (PPTX) from PDFs:

```bash
make pptx-ch01       # Single chapter PPTX
make pptx-vol1       # All Vol1 PPTX
make pptx            # All PPTX (both volumes)
```

PPTX conversion requires `poppler` (`brew install poppler` on macOS, `apt install poppler-utils` on Ubuntu) and `python-pptx` + `Pillow` (`pip install python-pptx Pillow`). The PPTX files are image-based at 300 DPI — visually identical to the PDF, suitable for presenter mode and annotations, but not editable text.

Output goes to `_build/vol1/` and `_build/vol2/` (PDFs and PPTX side by side).

### Editing Workflow

1. **Fork** the [repository](https://github.com/harvard-edge/cs249r_book)
2. **Edit** the `.tex` file for the chapter you want to change
3. **Rebuild** with `make chXX`
4. **Review** the PDF output in `_build/`

### SVG Figures

All diagrams are authored as **SVG files** in each chapter's `images/` directory. SVGs are the source of truth; the build system converts them to PDF via Inkscape for embedding in Beamer.

To modify a diagram:

1. Edit the `.svg` file in any vector editor (Inkscape, Illustrator, Figma)
2. Follow the SVG style guide (palette, typography, canvas conventions) used by the book figures
3. Rebuild the deck; the Makefile handles SVG-to-PDF conversion

### Theme Customization

The file `assets/beamerthememlsys.sty` controls the global look:

- **Accent color**: Deep Rose (`#BE185D`) used throughout
- **Header bar**: Section navigation with centered labels
- **Footer**: Book logo + chapter context (left), page number + institution (right)
- **Fonts**: Metropolis base with custom overrides

Change the accent color or institution branding in the theme file and all decks update on rebuild.

## Suggested Semester Plans

### 16-Week Plan: Volume I (Foundations)

```{=html}
<table class="slide-table">
  <thead>
    <tr>
      <th>Week</th>
      <th>Chapter</th>
      <th>Topic</th>
      <th>~Time</th>
    </tr>
  </thead>
  <tbody>
    <tr><td>1</td><td>0</td><td>Course Overview</td><td>53 min</td></tr>
    <tr><td>2</td><td>1</td><td>Introduction</td><td>74 min</td></tr>
    <tr><td>3</td><td>2</td><td>ML Systems</td><td>70 min</td></tr>
    <tr><td>4</td><td>3</td><td>ML Workflow</td><td>79 min</td></tr>
    <tr><td>5</td><td>4</td><td>Data Engineering</td><td>66 min</td></tr>
    <tr><td>6</td><td>5</td><td>Neural Network Computation</td><td>68 min</td></tr>
    <tr><td>7</td><td>6</td><td>Network Architectures</td><td>63 min</td></tr>
    <tr><td>8</td><td colspan="3" style="text-align:center; font-style:italic;">Midterm / Review</td></tr>
    <tr><td>9</td><td>7</td><td>ML Frameworks</td><td>73 min</td></tr>
    <tr><td>10</td><td>8</td><td>Model Training</td><td>68 min</td></tr>
    <tr><td>11</td><td>9</td><td>Data Selection</td><td>63 min</td></tr>
    <tr><td>12</td><td>10</td><td>Model Compression</td><td>64 min</td></tr>
    <tr><td>13</td><td>11</td><td>Hardware Acceleration</td><td>66 min</td></tr>
    <tr><td>14</td><td>12–13</td><td>Benchmarking + Model Serving</td><td>142 min</td></tr>
    <tr><td>15</td><td>14–15</td><td>ML Ops + Responsible Engineering</td><td>130 min</td></tr>
    <tr><td>16</td><td>16</td><td>Conclusion + Final Review</td><td>51 min</td></tr>
  </tbody>
</table>
```

### 16-Week Plan: Volume II (At Scale)

```{=html}
<table class="slide-table">
  <thead>
    <tr>
      <th>Week</th>
      <th>Chapter</th>
      <th>Topic</th>
      <th>~Time</th>
    </tr>
  </thead>
  <tbody>
    <tr><td>1</td><td>0</td><td>Course Overview: ML Systems at Scale</td><td>49 min</td></tr>
    <tr><td>2</td><td>1</td><td>Introduction</td><td>76 min</td></tr>
    <tr><td>3</td><td>2</td><td>Compute Infrastructure</td><td>72 min</td></tr>
    <tr><td>4</td><td>3</td><td>Network Fabrics</td><td>68 min</td></tr>
    <tr><td>5</td><td>4</td><td>Data Storage</td><td>69 min</td></tr>
    <tr><td>6</td><td>5</td><td>Distributed Training Systems</td><td>70 min</td></tr>
    <tr><td>7</td><td>6</td><td>Collective Communication</td><td>64 min</td></tr>
    <tr><td>8</td><td colspan="3" style="text-align:center; font-style:italic;">Midterm / Review</td></tr>
    <tr><td>9</td><td>7</td><td>Fault Tolerance and Reliability</td><td>68 min</td></tr>
    <tr><td>10</td><td>8</td><td>Fleet Orchestration</td><td>74 min</td></tr>
    <tr><td>11</td><td>9</td><td>Inference at Scale</td><td>71 min</td></tr>
    <tr><td>12</td><td>10</td><td>Performance Engineering</td><td>72 min</td></tr>
    <tr><td>13</td><td>11–12</td><td>Edge Intelligence + Ops at Scale</td><td>132 min</td></tr>
    <tr><td>14</td><td>13–14</td><td>Security/Privacy + Robust AI</td><td>142 min</td></tr>
    <tr><td>15</td><td>15–16</td><td>Sustainable AI + Responsible AI</td><td>131 min</td></tr>
    <tr><td>16</td><td>17</td><td>Conclusion + Final Review</td><td>47 min</td></tr>
  </tbody>
</table>
```

### 32-Week Combined Plan

For a two-semester sequence, teach Volume I in the first semester and Volume II in the second. Volume I provides the single-machine foundations that Volume II builds upon for distributed systems. The combined plan covers the full ML systems stack from data engineering on a single accelerator to fleet-scale governance.

## Audio Lectures {.coming-soon-section}

```{=html}
<div class="info-section">
  <h2>Coming Soon: Audio Lectures <span class="coming-soon">Coming Soon</span></h2>
  <p>
    Each slide deck will have a companion <strong>audio lecture</strong> generated from the
    speaker notes written by the textbook author. These recordings will be:
  </p>
  <ul>
    <li><strong>Synchronized</strong> with the slide deck for self-paced playback</li>
    <li><strong>Based on author notes</strong>, not generic AI narration</li>
    <li><strong>Designed for flipped classrooms</strong>: students listen before class, class time is for active learning</li>
    <li><strong>Downloadable</strong> as MP3 for offline use</li>
  </ul>
</div>
```