Upon the successful completion of this course, students will be able to: interpret key Artificial Intelligence (AI) concepts and history, describe the distinctions between AI types and models, and classify various AI systems in real-world contexts; analyze algorithms and intelligent agents, compute and interpret logarithmic functions and probability theories in AI, and evaluate data accuracy and precision; compare and contrast machine learning and AI, apply theoretical knowledge to develop AI-driven solutions, and implement AI programming languages like LISP and Prolog; design intelligent agents, evaluate decision-making models, and synthesize knowledge of Bayesian networks, neural networks, and decision trees to create AI solutions; and analyze AI's ethical implications, manage AI projects using advanced functionalities, and predict future AI technology directions and their societal impacts.