This course introduces foundational concepts and methods in artificial intelligence, including but not limited to supervised and unsupervised learning, deep learning, and reinforcement learning. The course highlights practical applications of emerging AI techniques in domains such as computer vision, natural language processing, and decision support, enabling students to connect theory with real-world use cases. Emphasis is placed on assessing the suitability, assumptions, and limitations of AI models for specific contexts. Learners will also consider the ethical and practical implications of AI deployment, including model bias and fairness, the possibility of discrimination when AI systems are trained on non-inclusive data, and examples of AI use in Indigenous language preservation.