Machine Learning & AI

This course introduces students to the ethical issues arising from the use of Artificial Intelligence.

Gain a robust understanding of deep learning through both theory and hands-on implementation, spanning domains such as computer vision, natural language processing (NLP) and graph data analysis. Explore neural network architectures, optimization techniques, and advanced models (CNNs, RNNs, GANs, GNNs). 

This course covers AI fundamentals in finance, Python basics, financial libraries (Numpy, Pandas, etc.), and SQL. It includes machine learning concepts, supervised/unsupervised learning, reinforcement learning, and algorithmic trading, concluding with a real-world case study application.

This course immerses students in the world of generative artificial intelligence (GenAI) and covers high-level browser-based tools, emphasizing the Python code behind them.

This course covers data engineering fundamentals, including data collection, storage, processing, and analysis. It explores the entire data pipeline and the technologies used, preparing students to apply these skills to real-world use cases in analytics and machine learning.

Introduction to linear algebra: Systems of linear equations Matrix algebra Linear independence Subspaces, bases and dimension Orthogonality Least-squares methods Determinants Eigenvalues…

Learn machine learning origins, principles, and practical applications, as well as implementation via the Python programming language. Students will learn to train a model, evaluate its performance, and improve its performance.

Learn machine learning origins, principles, and practical applications, as well as implementation via the R programming language. Students will learn to train a model, evaluate its performance, and improve its performance.