Machine Learning Tutorials

This is a five-session continuing-education ML tutorials series for civil engineering postgraduate students and teaching staff. We prioritise problem framing and data quality, then build from linear baselines and the perceptron through neural architectures to transformers, prompting, RAG, and light fine-tuning.

Department: Continuing-education workshops for civil engineering postgraduate students and teaching staff

Institution: Pontificia Universidad Católica del Perú (PUCP)

Lectures: 5

Hours: 15

From: October 05, 2026

To: November 30, 2026

Lectures

AI Systems and ML Pipeline

October 05, 2026

We introduce the course, place AI on one timeline that ends in the technocentric problem and explore the systems-engineering approach as a way to address it. As a result we define the ML pipeline, and the data-science process (access, assess, and address). A practical session implements the data science methodology from data preparation to training a simple linear model in the Civil Engineering context.

From Linear Models to the Perceptron

October 12, 2026

This lecture explores the idea of learning from data. We explore the induction process that agents follow to go from a specific set of observations to general rules. We will explore linear regression and classification models, building upon these foundations to introduce the perceptron as a fundamental building block for neural networks. We examine their mathematical formulation, learning process, and limitations, concluding with how these simple units form the basis for neural network architectures.

Neural Networks

October 19, 2026

We will continue our course exploring neural networks. We will build on previous concepts to define and formalise these supervised models that constitute the foundations of the latest advances in Machine Learning. We will also introduce and formalise Deep Learning models, their architectures and implementation details.