Excerpt from course description

Predictive Modelling with Machine Learning

Introduction

This course provides a rigorous introduction to predictive modelling using traditional machine learning methods. The course emphasizes understanding of statistical principles, model selection, and the practical implementation of algorithms to solve real-world business or societal problems.

The vast amount of widely available data allows machines to solve challenging tasks without explicitly being programmed to do so, often outperforming existing methods based on domain knowledge and/or human experts.

In this course we focus on basic machine learning methods, both for supervised and unsupervised learning. We will look at how exactly machines "learn" from the data, and how they use the knowledge learned during training to solve tasks of interest.

The course covers both the theory (looking at the methods more rigorously than introductory machine learning courses) and the practice of machine learning (using Python).

Course content

The course focuses on "Traditional Machine Learning" methods, providing a deep dive into the algorithms that form the bedrock of data science before the introduction of Deep Learning.

  1. Statistical foundations: Recap of probability & inference; bias/variance trade‑off; over‑fitting.

  2. Data Preparation: Feature engineering, data cleaning, and preprocessing pipelines.

  3. Linear & Penalised regression: Ridge, lasso, elastic‑net; diagnostics and interpretability.

  4. Classification algorithms: k‑NN, Naïve Bayes, Support Vector Machines (SVM), and Logistic Regression.

  5. Tree-based methods & Ensembles: Decision trees, Bagging, Random Forests, and Gradient Boosting (e.g., XGBoost).

  6. Unsupervised Learning: Clustering (k‑means, hierarchical) and Dimensionality Reduction (PCA).

  7. Model assessment & Selection: Cross‑validation, bootstrap, information criteria, learning curves.

  8. Responsible ML: Fairness, interpretability, and reproducibility.

  9. Introduction to Neural Networks: A brief conceptual bridge connecting linear models to basic Multilayer Perceptrons (MLP), distinguishing them from Deep Learning architectures.

Disclaimer

This is an excerpt from the complete course description for the course. If you are an active student at BI, you can find the complete course descriptions with information on eg. learning goals, learning process, curriculum and exam at portal.bi.no. We reserve the right to make changes to this description.