Introduction to Machine Learning
Course Code: Y1B1
ECTS Credits: 9.0
Course Description
This course offers a hands-on introduction to the fundamental concepts and techniques in machine learning. Structured around practical DataLab tasks, students will develop classification, regression, and clustering models, starting from raw data to model evaluation and improvement. The course combines essential theory with interactive coding sessions using Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.
Through guided tutorials, notebook-based exercises, and model-building challenges, students will learn to preprocess data, select features, train baseline models, evaluate them using proper metrics, and iterate on their solutions using techniques like hyperparameter tuning, dimensionality reduction (PCA, t-SNE, UMAP), and model selection strategies. Key algorithms such as decision trees, random forests, and ensemble methods will be covered in depth. By the end of the course, students will have a clear understanding of the full machine learning workflow and the ability to build robust, data-driven solutions.
Course Content
- Data Processing for Machine Learning
- Classification
- Regression
- Clustering
- Dimensionality Reduction
- Feature Engineering and Selection
- Model Selection
- Hyperparameter Tuning
- Overfitting and Underfitting
- Performance Metrics
- Cross-validation
- Error Analysis
Prerequisites
- Introduction to Programming with Python (Y1A1)
Recommended Reading
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd Ed.) by Aurélien Géron