This page covers the full structure of the course: what is taught, when, and how you are assessed.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/domingomery/vision/llms.txt
Use this file to discover all available pages before exploring further.
The course runs August–November 2025 at Pontificia Universidad Católica de Chile. There are 28 classes, meeting on Tuesdays and Thursdays. Professor: Domingo Mery.
Learning progression
The course is organized into five chapters. Each chapter builds on the previous one, moving from foundations to application to responsibility.Cap00 — General lines
Course presentation, bibliography, and exam preparation. Establishes expectations and provides the reference materials used throughout the semester.
Cap01 — Introduction
Definitions of computer vision, motivating applications, and a two-part history of the field from early perspective geometry to the deep learning era.
Cap02 — Computational geometry
The mathematical backbone of the course. Homogeneous coordinates, 2D and 3D transformations, homographies, camera calibration, RANSAC, epipolar geometry, trifocal geometry, and 3D reconstruction.
Cap03 — Deep learning
Convolutional neural networks, object detection (YOLO), facial analysis, semantic segmentation (UNet), generative adversarial networks (GANs), anomaly detection, CLIP, Transformers, Visual Transformers (ViT), and diffusion models.
Full 28-class schedule
Cap00 — General lines (Classes 1 and 28)
Cap00 — General lines (Classes 1 and 28)
| Class | Date | Topics |
|---|---|---|
| 01 | Thu 07-Aug-2025 | Course presentation, bibliography overview |
| 28 | Thu 27-Nov-2025 | Exam support — past exam questions (2023, 2024) and solutions |
Cap01 — Introduction (Classes 2–4)
Cap01 — Introduction (Classes 2–4)
| Class | Date | Topics |
|---|---|---|
| 02 | Tue 12-Aug-2025 | Definitions (CV01); History part 1 |
| 03 | Thu 14-Aug-2025 | History (cont.); vanishing points and perspective |
| 04 | Tue 19-Aug-2025 | History part 2 |
Cap02 — Computational geometry (Classes 2–11)
Cap02 — Computational geometry (Classes 2–11)
| Class | Date | Topics |
|---|---|---|
| 02 | Tue 12-Aug-2025 | Homogeneous coordinates, points, lines, planes |
| 03 | Thu 14-Aug-2025 | 2D–2D transformations, homographies; in-class exercise E01 (John Lennon) |
| 04 | Tue 19-Aug-2025 | Homographies (cont.); 3D transformations |
| 05 | Thu 21-Aug-2025 | 3D–3D and 3D–2D transformations; in-class exercise E02 (clock rectification) |
| 06 | Tue 26-Aug-2025 | Parameter estimation, calibration, RANSAC |
| 07 | Thu 28-Aug-2025 | Mosaics; SIFT features; camera calibration |
| 08 | Tue 02-Sep-2025 | 3D reconstruction; calibration (Python and MATLAB) |
| 09 | Thu 04-Sep-2025 | Epipolar geometry; in-class exercise E04 |
| 10 | Tue 09-Sep-2025 | Epipolar geometry (cont.); multiple-view X-ray applications |
| 11 | Thu 11-Sep-2025 | Trifocal geometry; chapter summary |
Cap03 — Deep learning (Classes 12–23 and 25)
Cap03 — Deep learning (Classes 12–23 and 25)
| Class | Date | Topics |
|---|---|---|
| 12 | Tue 23-Sep-2025 | Introduction to deep learning; CNNs |
| 13 | Tue 30-Sep-2025 | CNN training; in-class exercise E05 |
| 14 | Thu 02-Oct-2025 | Object detection — YOLO + tracking |
| 15 | Tue 07-Oct-2025 | YOLO (cont.); in-class exercise E06 (mask detection) |
| 16 | Thu 09-Oct-2025 | Facial analysis |
| 17 | Tue 14-Oct-2025 | Facial analysis — social; face recognition (AdaFace); in-class exercise E07 |
| 18 | Thu 23-Oct-2025 | Semantic segmentation — UNet; in-class exercise E08 |
| 19 | Tue 28-Oct-2025 | Generative adversarial networks (GANs); detection statistics |
| 20 | Thu 30-Oct-2025 | GAN in-class exercise E09; anomaly detection |
| 21 | Tue 04-Nov-2025 | CLIP |
| 22 | Thu 06-Nov-2025 | Transformers from scratch |
| 23 | Tue 11-Nov-2025 | Visual Transformers (ViT); HuggingFace; in-class exercise E10 |
| 25 | Tue 18-Nov-2025 | Stable diffusion; diffusion models |
Cap04 — Ethics & AI (Classes 24–27)
Cap04 — Ethics & AI (Classes 24–27)
| Class | Date | Topics |
|---|---|---|
| 24 | Thu 13-Nov-2025 | The good, the bad, and the ugly of AI; essay assignment T03 released |
| 25 | Tue 18-Nov-2025 | Ethical challenges in facial recognition |
| 26 | Thu 20-Nov-2025 | Federated / swarm learning; explainability; Chilean data-protection law (Ley 19628) |
| 27 | Tue 25-Nov-2025 | Bias and fairness; good practices; explainability with MinPlus; adversarial attacks; quiz E11 |
The essay assignment T03 is described on Canvas. The quiz E11 in Class 27 is also submitted via Canvas.
Grading and assignments
The course has three main assignments plus quizzes:| Item | Description |
|---|---|
| T01 | Assignment 1 — covers computational geometry (Cap02) |
| T02 | Assignment 2 — deep learning project (released Class 18, Cap03) |
| T03 | Essay — ethics and AI (released Class 24, Cap04) |
| E01–E11 | In-class exercises throughout the semester |
| Examen | Final exam — past papers from 2023 and 2024 are available for practice |
Tools and environment
The course uses two primary computing environments:- Python / Google Colab — the primary tool for all exercises and assignments. Notebooks run in the browser with free GPU access.
- MATLAB — used for selected geometry and calibration demonstrations.