AI Week: Synergizing Human and AI for Multidisciplinary Solutions (SHAMS)

AI Course Module for PhD Students

Lecturers: Prof. Dr. Shadi Albarqouni

Tutors: TBA

Time: 23-27 June 2025 (tentative)

Course Description

This 5-day intensive course is tailored for PhD students from diverse disciplines seeking a robust introduction to Artificial Intelligence (AI) and Machine Learning (ML). The module begins with essential mathematical concepts, including algebra, probability, and optimization, before moving into fundamental and advanced machine learning techniques. Students will explore deep learning architectures, engage in hands-on coding exercises, and apply their knowledge to real-world problems, with a particular focus on healthcare and resource-constrained environments. The course also addresses the ethical implications of AI and the importance of explainability in AI models, preparing students to implement AI solutions responsibly in their research.

Course Objectives

Successful students should

• Gain a solid foundation in AI and Machine Learning: Understand key concepts and techniques, including machine learning theory, neural networks, and deep learning, and their applications across various fields.
• Identify and evaluate machine learning algorithms: Learn to identify the strengths and weaknesses of various machine learning algorithms, and report on performance measures and evaluation metrics.
• Formulate and solve AI-related problems: Be able to formulate machine learning problems relevant to a wide range of applications and solve moderately complex problems using machine learning algorithms.
• Apply AI to multidisciplinary challenges: Implement, adapt, and optimize machine learning algorithms for real-world problems in areas such as climate change, agriculture, water management, and healthcare.
• Develop practical skills with AI tools: Gain hands-on experience using Python and PyTorch to build, experiment with, and optimize AI models.
• Explore ethical and societal impacts of AI: Understand the importance of fairness, transparency, and explainability in AI, particularly in sensitive and critical applications.
• Foster interdisciplinary collaboration: Work with peers from diverse disciplines to innovate and develop AI-driven solutions for complex global challenges.

Target Audience

The target audience for AI Week SHAMS includes interdisciplinary researchers and development-focused scholars from diverse fields such as economics, political science, sociology, environmental science, and natural sciences, all united by a commitment to advancing sustainable development goals. This program is designed for individuals with a strong interest in tackling global challenges, including climate change, water resources, agriculture, food security, health, and poverty reduction. Additionally, it is ideal for early-career academics, PhD students, postdoctoral researchers, and practitioners eager to integrate AI and machine learning into their research or practical work, particularly within development contexts.

Prerequisites

• Mathematical Background: A basic understanding of linear algebra (vectors, matrices), probability theory (random variables, distributions), and calculus (derivatives).
• Interest in AI/ML: A keen interest in AI and Machine Learning, with the intent to apply these techniques in research, particularly in interdisciplinary fields such as healthcare.

Desired:

• Programming Skills: Proficiency in Python, including familiarity with libraries like NumPy and Pandas, and basic experience with PyTorch.

Credit Hours

The AI Week SHAMS program is designed to offer an intensive learning experience with a total of 27 actual contact hours, which corresponds to 1.5 ECTS credits.

Course Content

• Foundations of AI and Machine Learning: Introduction to key concepts, algorithms, and mathematical foundations, including algebra and probability theory.
• Supervised and Unsupervised Learning: Deep dive into logistic regression, linear regression, clustering, and dimensionality reduction techniques, with hands-on labs.
• Neural Networks and Deep Learning: Exploration of neural network architectures and deep learning techniques, along with practical implementation.
• AI Applications in Real-World Challenges: Focused sessions on applying AI to critical areas such as agriculture, food security, climate change, healthcare, and economics.
• Interactive Group Discussions: Collaborative discussions where participants brainstorm AI solutions to their specific PhD research challenges, using whiteboards and sticky notes.
• Final Project Presentations: Participants present conceptual proposals for integrating AI into their research, evaluated on clarity, creativity, and feasibility.

Detailed Schedule (tentative)

Group Discussion and Final Project (TBD)

Evaluation Criteria (TBD)