📋 Program — Machine Learning with Business Applications

🎓 5 ECTS 📍 Universidad Técnica Federico Santa María Compulsory · GEME track

1. Course identification


Course title	Machine Learning — Business Applications
Programme	EGEI · Track GEME (Globalisation and Emerging Market Economies)
ECTS	5
Prerequisites	Applied Econometrics I and II
Lecturer	Sebastián Azócar M., MSc in Data Science
Contact	hizocar@gmail.com
Language	English
Schedule	Fridays, 13:00 – 16:00 · 11 September – 27 November 2026 (11 sessions)
Tools	Python · scikit-learn · an AI coding assistant of your choice

2. Course description

Machine learning (ML) uses algorithms that learn patterns from data instead of following rules written by hand. This course covers the statistical methods and techniques used in ML to make predictions with business applications: what a model can and cannot learn from data, how to evaluate it honestly, and how to turn a prediction into a decision someone can act on.

What changes this year. Code assistants have moved from novelty to standard equipment on a data team. Producing code is cheap now; being accountable for it is not. The course therefore runs two threads in parallel:

the modelling thread — the statistics and algorithms, which do not change because a machine writes the syntax for you; and
the workflow thread — using an assistant to scaffold, refactor, debug and explain code, and verifying its output with the same scepticism you would apply to a colleague's pull request.

The second thread is not a bonus topic: an assistant that produces a plausible-looking model with a silent data leak is worse than no assistant at all. Learning to catch that is part of the course.

3. Learning outcomes

On completion, students will be able to:

Frame a business question as a supervised or unsupervised learning problem, and state what a good answer would look like.
Prepare and explore real data in Python, reproducibly.
Fit and tune regression and classification models, and justify the choice of method.
Evaluate models honestly: train/test discipline, cross-validation, the bias–variance trade-off and the right metric for the business problem.
Reduce and segment data without labels using principal components and clustering.
Work with an AI coding assistant productively: prompt it precisely, review its output, test it, and identify when it is wrong.
Communicate results, assumptions and limitations to a non-technical audience.

4. Learning methods and schedule

Learning by doing. Sessions last three hours and follow the same rhythm: a concept block, a hands-on lab paired with your assistant, and applied or project work. Students are expected to read the assigned material before class, and a study load of at least three hours per week outside the classroom is assumed. All assessed work is project-based.

Calendar

#	Date	Session
1	Fri 11 Sep ✅	Data and decision making · VS Code and assistant setup
—	Fri 18 Sep	🇨🇱 No class — Fiestas Patrias
2	Fri 25 Sep	Data manipulation with pandas
3	Fri 2 Oct	What is machine learning? Fitting, KNN, bias–variance
4	Fri 9 Oct	The ML pipeline: cross-validation and data leakage · Project 1 released
5	Fri 16 Oct	Classification I: logistic regression, confusion matrix, ROC
6	Fri 23 Oct	Project 1 presentations · Classification II: naïve Bayes and text
7	Fri 30 Oct	Trees and ensembles · Project 2 released
8	Fri 6 Nov	Variable selection and shrinkage: LASSO, Ridge, Elastic Net
9	Fri 13 Nov	Unlabeled data: PCA and clustering
10	Fri 20 Nov	Introduction to neural networks · project clinic
11	Fri 27 Nov	Project 2 final presentations and course closing

🗓️ The detailed plan for each session, with objectives, labs and notebooks, is on the Classes page.

5. Course contents

A. Data and decision making

Different types of data · data manipulation · from a business question to a dataset

B. What is machine learning?

B.1 ML models: what a model is · fitting a model · KNN · polynomial regression · overfitting and underfitting (bias vs. variance) · the cost function · training and test error
B.2 The ML pipeline: the bias–variance trade-off · cross-validation · applying the pipeline

C. Classification

C.1 Logistic regression: what classification is · technique and methodology · measuring model performance · the ROC curve
C.2 Generative models: the naïve Bayes classifier · text classification · NLP application: measuring text sentiment

D. Trees and forests

D.1 Tree-based methods: structure of decision trees · loss functions · pruning · regression and classification trees
D.2 Ensemble methods: bagging · random forests · boosting

E. Selection and shrinkage

E.1 Variable selection: best subset selection · stepwise, forward and backward selection
E.2 Shrinkage methods: shrinkage vs. selection · LASSO · Ridge · Elastic Net

F. Unlabeled data

F.1 Dimension reduction: principal components and their application
F.2 Clustering: k-means · hierarchical clustering · advantages and limitations

G. Introduction to neural networks

G.1 Foundations: the simple perceptron · structure of an ANN · methods
G.2 Implementation: training the model · advantages, limitations and optimization

H. Working with AI coding assistants (transversal)

Prompting for code that is specific enough to be useful · reading and reviewing generated code · testing before trusting · typical failure modes in data work (leakage, silent type coercion, fabricated APIs, wrong metric) · reproducibility · disclosure and academic honesty

📖 The full guide for this thread is on the Code Assistants page.

6. Assessment

All of the final grade comes from two projects. There are no written exams and no separate in-class activities.

Assessment	Weight	Covers	Due	Presented
Project 1 — supervised learning on a business problem	40%	Contents A, B, C	Thu 22 Oct, 23:59	Fri 23 Oct
Project 2 — end-to-end ML case, student-chosen data	60%	All contents, D–G included	Thu 26 Nov, 23:59	Fri 27 Nov
Total	100%

Each project is delivered as a reproducible notebook + short report + live presentation, and includes a mandatory assistant log: which tool you used, for what, and what you had to correct. Full instructions, deadlines and the grading rubric are on the Projects page.

Final grade = 0.40 × Project 1 + 0.60 × Project 2

Assistant use: allowed, expected, and disclosed

Using an AI coding assistant is permitted and encouraged in both projects. Two rules apply: (1) you must be able to explain every line you submit — presentations include questions about your own code; (2) you must disclose what you delegated in the assistant log. Code you cannot explain is treated as code you did not write.

7. Readings

Foundations

Models, pipeline and evaluation

Working with code assistants

📌 ISLR = James, Witten, Hastie & Tibshirani, An Introduction to Statistical Learning, freely available at statlearning.com.