Artificial Intelligence 1
Second semester 2026 · Foundations of Artificial Intelligence.
Description
The course offers a hands-on tour through data, classic machine learning, deep learning, computer vision, NLP, agents and MLOps. The goal isn’t to go deep on every technique, but for students to be able to frame a problem, build a baseline, compare alternatives, analyze errors and communicate a decision backed by evidence.
Taught over 14 sessions (2 holidays). Each session combines brief preparation, explanation, hands-on practice, and a compact individual deliverable.
Schedule
2026 edition, second semester.
| Class | Date | Main topic |
|---|---|---|
| 1 | August 3 | What is AI, the AI/ML/DL relationship, CRISP-DM and EDA on a real dataset |
| 2 | August 10 | Modelable data, baseline, regression and MLflow. Case 1 launch |
| 3 | August 17 | Classification, metrics and cost of error |
| 4 | August 24 | Clustering, PCA and interpretation |
| 5 | August 31 | Neural networks, MLPs and backpropagation |
| 6 | September 7 | Optimization, regularization and evidence-based model selection |
| 7 | September 14 | Midterm 1 · Introduction to CNNs, transfer learning and augmentation. Case 2 launch |
| 8 | September 21 | Detection, segmentation and visual explainability |
| — | September 28 | No class (Semana UCU) |
| 9 | October 5 | Transformers, tokenization, embeddings and semantic search |
| — | October 12 | Holiday — async activity: semantic retrieval |
| 10 | October 19 | LLMs, prompting, RAG, grounding and evaluation |
| 11 | October 26 | Agents, ReAct, LangGraph, MCP, tools, permissions and traces |
| — | November 2 | Holiday — async activity: traces, permissions, guardrails |
| 12 | November 9 | Midterm 2 · Introduction to MLOps and the end-to-end pipeline |
| 13 | November 16 | Pipeline, release, rollback and final exam prep |
| 14 | November 23 | Final exam: quiz, individual modification, group presentation and defense |
Grading
| Item | Format | Points |
|---|---|---|
| Five iRAT/tRAT | Individual and team | 10 |
| Case 1 (portfolio) | Team | 7.5 |
| Case 2 (portfolio) | Team | 7.5 |
| Midterm 1 | Individual, proctored | 20 |
| Midterm 2 | Individual, proctored | 20 |
| Final exam | Individual + group defense | 35 |
Passing grade from 65%, with a minimum of 40% on the final exam. Teams of three students are the preferred format (pairs allowed when enrollment requires it).