This teaching plan offers a structured exploration of artificial intelligence (AI) applications in materials science, with adaptable content depth tailored to various expertise levels. It encompasses introductory lectures on AI relevance in materials science, progressing to data science fundamentals and hands-on activities. The curriculum includes AI tools, supervised and unsupervised learning techniques, and data-driven optimization strategies applicable to materials science. Key topics such as Convolutional Neural Networks, boosting algorithms, dimensionality reduction techniques, and Bayesian optimization are covered in depth()3-month timetable details weekly sessions, including lectures, practical exercises, and assignments to reinforce learning. Students will engage in hands-on projects to apply AI techniques to real-world materials science problems, culminating in a comprehensive exam and final project. The plan emphasizes collaborative learning and flexibility to accommodate varying student backgrounds.