Introduction to Python programming and artificial intelligence. Programming emphasis on techniques used in artificial intelligence. Jupyter Notebooks, NumPy, pandas, Matplotlib.

Fundamental concepts, algorithms, and techniques in machine learning, along with an overview of the necessary mathematics. Basic principles underlying regression, classification, and unsupervised learning.

Best practices of software engineering for machine learning and artificial intelligence. Effective data visualization and exploratory data analysis.

Key concepts of neural networks and deep learning. Selecting appropriate architecture, training, evaluation of neural networks. Convolutional neural networks, transformers.

Special topics in machine learning and artificial intelligence of particular relevance to applications in industry, including use of large-language models.

Research topics in machine learning and artificial intelligence. Emphasis on recent developments and applications in the physical sciences.

Fundamental concepts of artificial intelligence through programming projects. Data preprocessing, model evaluation, interpretation of results, managing large datasets, cloud-based resources.

Fundamental concepts of artificial intelligence through programming projects. Emphasis on adapting and applying state-of-the-art neural networks.

Capstone project applying techniques from artificial intelligence to a problem related to the physical sciences.

Seminar on ethical issues related to machine learning and artificial intelligence.

Seminar preparing students for careers in artificial intelligence. CV/resume, portfolio, interview skills, the AI/ML job landscape.