Machine Learning — EGEI
Learn the foundations of machine learning while working the way data teams actually work today: side by side with an AI coding assistant. You will not just prompt it — you will read, test and defend every line it writes.
🗓️ Session-by-session plan and dates in Classes · syllabus in Program · deliverables in Projects.
📌 What this course is about
Machine learning has two halves. One is statistical: what a model is, what it can and cannot learn from data, and how to tell a real signal from noise. The other is craft: turning a business question into data, code and a decision someone can act on.
This year the craft half changes. Writing code is no longer the bottleneck — reviewing it is. So every session pairs a modelling topic with the corresponding assistant skill: how to ask for the right thing, how to check the answer, and how to recognise when the assistant is confidently wrong.
🧠 Modelling foundations
Regression, classification, resampling, regularization and an introduction to neural networks —
with emphasis on why a model works, not only on calling .fit().
🤖 Assistant-driven workflow
Use Copilot, Claude, ChatGPT or Cursor as a pair programmer: scaffolding, refactoring, debugging and explaining code — under a verification discipline you can defend.
📈 Two real projects
Everything is assessed through two end-to-end projects on real data, delivered with code, a short report and a live presentation.
🎯 Learning outcomes
By the end of the course you will be able to:
- Frame a business question as a supervised learning problem, and say what a good answer looks like.
- Prepare, explore and visualize a real dataset in Python, reproducibly.
- Choose, fit and tune regression and classification models, and justify the choice.
- Evaluate models honestly: train/test discipline, cross-validation, uncertainty and the right metric.
- Work with an AI coding assistant productively — and verify its output rather than trusting it.
- Communicate results and limitations to a non-technical audience.
🧰 What you need
| Python environment | Local install (Anaconda/uv/venv) or Google Colab — both work |
| Code assistant | At least one of: GitHub Copilot, Claude, ChatGPT, Gemini, Cursor (free tiers are enough) |
| Background | Basic programming and basic statistics; no prior ML required |
📖 See the Code Assistants page for setup, prompting patterns, the verification checklist and the course policy on assistant use.
👤 Instructor
Sebastián Azócar
Data Scientist @ LATAM Airlines Group · Lecturer
Mathematical Engineer (USACH) with an MSc in Data Science (Universidad del Desarrollo). Over five years applying machine learning in industry — Caja Los Andes, Walmart Chile and currently LATAM Airlines Group — and more than a decade teaching data science and statistics.