This AI course for data science undergraduates covers essential topics, starting with an overview of AI and intelligent agents. It delves into various search strategies, including uninformed and informed search, as well as local and adversarial search techniques. The course then transitions to machine learning (ML) and neural networks, including specialized architectures like convolutional neural networks (CNNs). It also examines the role and implementation of expert systems. Finally, it addresses ethical issues in AI, emphasizing the importance of responsible AI practices. Through a blend of theoretical knowledge and practical applications, students will gain a solid foundation in AI and its relevance to data science.