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The Machine Learning Lab, using Python and the Jupyter Notebook environment, is the beating heart of modern Computer Engineering and Artificial Intelligence specialisations. Here the student moves from the dry study of mathematical algorithms to building intelligent systems that predict, classify and make decisions based on data.
Vision
For the laboratory to be a leading incubator of software innovation and applied research, aimed at preparing a generation of engineers capable of leading the smart transformation and developing innovative data-driven solutions to meet the challenges of technology and industry.
Mission
To provide an interactive, open-source learning environment that enables students to link the theoretical concepts of machine learning with professional software implementation. The laboratory focuses on refining data engineering skills and on building and evaluating intelligent models using Python and the Jupyter Notebook environment, in line with the standards of the modern labour market.
Core Laboratory Objectives (Lab Objectives)
1- Mastering the interactive environment: enabling students to use Jupyter Notebook to write code, display charts and document results in one place, in a simplified step-by-step manner.
2- Data management and engineering: training students to clean data, handle missing values and transform raw data into formats suitable for training, using libraries such as Pandas and NumPy.
3- Building intelligent models: giving students the ability to apply various machine learning algorithms (such as Decision Trees, neural networks and SVM) through the Scikit-Learn library. Data analysis and visualisation: developing charting skills and extracting hidden patterns in data using the Matplotlib and Seaborn libraries.
4- Performance evaluation: training students to measure model performance accurately using scientific metrics such as Accuracy, the Confusion Matrix and the ROC curve.