Overview

Machine learning is often taught as a sequence of algorithms and code examples. This course begins instead with the statistical problem: how can a model learn a useful relationship from a finite and imperfect sample?

Learners move from data representation and loss functions to regression, classification, model complexity, validation and generalization. Computation is used to test ideas, not to replace them.

The course adapts the spirit of Gordon School of Business quantitative training to a shorter public format and provides a bridge into more mathematical study.

Learning objectives

By the end of the course, learners should be able to:

• formulate prediction problems using data, targets, features and loss functions;
• explain training, validation and generalization;
• interpret regression and classification models mathematically;
• recognize overfitting and the role of regularization;
• compare model performance using appropriate evidence;
• use simple computational experiments to diagnose model behaviour.

Intended participants and prerequisites

Course structure

The course is organised into six units. Each unit combines concise explanation, worked examples and short analytical exercises.

Unit	Subject	Principal questions
1	Learning from data	What exactly is a model trying to estimate?
2	Regression	How do linear models connect prediction, loss and estimation?
3	Classification	How are probabilities and decision boundaries used to classify observations?
4	Model complexity	Why does fitting observed data too closely damage future performance?
5	Validation and generalization	How should models be evaluated on unseen data?
6	Model failure	How can bias, leakage and unstable data create convincing but useless results?

Teaching and assessment

Teaching is delivered through concise lectures, technical notes, worked examples and guided exercises. AI-supported interaction may be used to explain concepts, generate additional practice and challenge a learner's reasoning, but assessment remains based on the learner's demonstrated understanding.

Component	Purpose	Weight
Short exercises	Check understanding of the principal concepts	40%
Applied investigation	Use the course framework on a new problem or dataset	30%
Final assessment	Integrated reasoning across the course	30%

A certificate of completion is issued only when the required assessments have been completed at the stated standard. Participation alone does not constitute completion.

Faculty

Materials and technical requirements

Learning environment

Learners need a current web browser and access to the Gordon learning platform. Where a course uses code, spreadsheets, datasets or external readings, the relevant requirements are identified inside the unit.

Course materials

Required readings, datasets and practice materials are identified within each unit. Public courses are designed so that required core materials can be accessed without a separate tuition charge.