The lecture for the "Introduction to Artificial Intelligence" (2V+2Ü), tailored for Bachelor of Computer Science students, will provide a foundational overview of AI, covering essential topics across three main areas. The first area is machine learning, where classic methods such as k-nearest neighbors, k-means clustering, and polynomial regression will be introduced to illustrate traditional approaches for solving data-driven problems. The second area focuses on deep learning, including an introduction to neural networks, multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and transformers, with applications in tasks like image recognition and natural language processing. The lecture will conclude with an introduction to large language models, exploring how to use, prompt, and create agents with these models, highlighting practical applications and emerging possibilities in AI. In the exercises, students will engage with programming concepts in Python, reinforcing the theoretical knowledge covered in lectures. Basic knowledge of vector and matrix operations, distributions, and programming is required for this course. This lecture aims to lay the groundwork for understanding AI fundamentals and prepares students for more advanced topics in the course.