Practical Artificial Intelligence and Applications

Catalog description

This intensive, hands-on course is designed for doctoral students seeking to apply AI in real-world contexts. Emphasizing practical applications, it focuses on solving real-world problems through case studies while minimizing theoretical discussions. Students will work with popular tools such as Scikit-learn, Pandas, Numpy, Matplotlib, PyTorch, Huggingface Transformers, Fastai, and Google Colab to gain tangible skills in AI and data science. The course begins with Python programming, building a foundation for implementing various machine learning models. It then delves into deep learning techniques, including convolutional neural networks (CNNs) and transformers, applied to various data types such as images and text. Through structured exercises and projects, students will refine their ability to select, build, and deploy models to address complex challenges, particularly in social and health sciences. By the end of the course, students will be adept at using advanced AI tools and platforms and equipped to create effective solutions in diverse fields. With a focus on real-world relevance and practical skills, this course prepares students to harness AI for solving pressing societal challenges.