Introduction to Artificial Intelligence

IntroAI
Abstract

Abstract

This course offers an introductory yet comprehensive survey of the foundational principles, methods, and applications of Artificial Intelligence (AI) for first-year engineering students. It traces the historical evolution of AI and builds an understanding of core learning paradigms, including predictive and generative modeling, decision-making systems, and modern large-scale architectures. Emphasis is placed on conceptual understanding, practical relevance, and critical thinking around the technological and societal implications of AI. A final practical component introduces students to the use of contemporary large language models and retrieval-based AI systems.

 

Teaching and Learning Methods

Lectures and hands-on tutorials

 

Course Policies

Mandatory presence (additional details shared during the first lecture)

Bibliography

Bibliography

Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.)

Luger, G. F. & Stubblefield, W. (2009). Artificial Intelligence: Structures and Strategies for Complex Problem Solving

Requirements

Prerequisites

Basic math (calculus, probability, linear algebra, ...)

Description

Description

  1. Foundations of AI – History, Concepts, and Architectures. Introduces the origins and evolution of AI, the shift from symbolic to data-driven methods, and the conceptual foundations of learning and deep neural architectures.
  2. Learning to Predict – Supervised Learning Concepts. Explains how AI systems infer input-output mappings from data using predictive models, highlighting key principles like training, generalization, loss minimization, and model complexity.
  3. Machines That Generate – Images, Text, and Creativity. Covers generative modeling approaches that produce new data, contrasting autoregressive and latent-based methods, and exploring how models learn to synthesize coherent content.
  4. Decision-Making AI. Presents agents that learn to act through interaction, introducing reinforcement learning, exploration-exploitation trade-offs, and preference-based feedback mechanisms.
  5. Engineering AI Systems – Hardware, Frameworks, and Pipelines. Surveys the computational infrastructure behind AI, including GPUs, software libraries, and model deployment workflows critical for training and inference.
  6. Ethics and Safety in AI. Examines societal and technical challenges of AI deployment, addressing issues like bias, transparency, control, and long-term safety.
  7. Practical AI – Using Large Language Models and Retrieval-Augmented Generation. Offers hands-on exposure to modern AI tools, showing how LLMs interact with structured knowledge via prompts and retrieval mechanisms.

 

Learning Outcomes

  1. Describe the fundamental concepts of modern AI systems, including prediction, generation, and decision-making, and distinguish between the major learning paradigms that support them.
  2. Explain how AI models learn from data, including the roles of representation, supervision, evaluation, and system design in building effective learning systems.
  3. Identify and discuss key challenges in AI, including ethical risks, safety concerns, and the limitations of current technologies in real-world contexts.

 

Evaluation

Final exam (100%)