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Introduction to Artificial Intelligence

Course Number: 27000170, 3 Credits

School of Management and Engineering, Nanjing University

Artificial Intelligence (AI) has rapidly evolved from a niche academic pursuit into one of the most defining and transformative fields within Computer Science (CS). If Operating Systems and Networks provide the infrastructure and communication channels for software, an AI course teaches you how to give that software the ability to learn, reason, and make decisions without explicit programming.

Notice

Part of the contents in this page and its sub-pages are not covered in the scope of course teaching and assessment, and this page is not an official course page, which is for reference only. Please refer to the actual course.

Position in Computer Science

In traditional CS courses (like Data Structures or OS), you learn to write deterministic algorithms: if you give the computer a specific input, it follows your exact step-by-step instructions to produce a guaranteed output.

An AI course marks a major paradigm shift toward probabilistic and heuristic problem-solving. You learn how to build systems that figure out the rules themselves by analyzing data, recognizing patterns, or exploring massive decision spaces.

AI is typically an upper-level elective or a core course in a specialized AI/Data Science track. It sits at the top of the CS pyramid because it relies heavily on several foundational subjects:

  • Mathematics: Linear algebra (for data manipulation), calculus (for optimizing models), and probability/statistics (for dealing with uncertainty).
  • Algorithms: Advanced search algorithms and optimization techniques.
  • Programming Maturity: Usually taught using Python, C++, or specialized frameworks, requiring strong, independent coding skills.

Main Contents

An introductory AI course usually starts with classical (symbolic) AI before moving into modern, data-driven Machine Learning (ML). The core topics typically include:

  • Search and Optimization: How a computer navigates millions of possibilities to find a solution. This includes pathfinding algorithms (like A* search, used in GPS) and game-playing algorithms (like Minimax, used in chess engines).
  • Machine Learning (ML): Teaching computers to learn from data. This is usually broken down into:
  • Supervised Learning: Learning from labeled data (e.g., predicting house prices based on historical sales).
  • Unsupervised Learning: Finding hidden structures in unlabeled data (e.g., clustering customers into market segments).
  • Reinforcement Learning: Training agents to make sequences of decisions by rewarding good behavior (used heavily in robotics and game-playing AI).
  • Artificial Neural Networks & Deep Learning: The mathematical models inspired by the human brain. Students learn how networks "train" through processes like backpropagation to handle incredibly complex tasks.
  • Natural Language Processing (NLP) & Computer Vision (CV): Introductions to how AI processes human language (text and speech) and visual information (images and video).

Practical Applications

AI has moved out of the lab and into nearly every sector of the modern economy. Its practical applications are vast:

  • Generative AI & LLMs: Large Language Models (like myself) and image generation models are used for content creation, coding assistance, customer service, and complex data summarization.
  • Autonomous Systems: Self-driving cars and delivery drones rely on Computer Vision to map their surroundings and Reinforcement Learning to navigate safely in real-time.
  • Recommendation Engines: The algorithms that curate your social media feeds, suggest what to watch on Netflix, or recommend products on Amazon are all driven by machine learning models analyzing your past behavior.
  • Finance and Cybersecurity: AI systems process millions of transactions per second to detect fraudulent credit card activity, model stock market trends, or identify anomalous network traffic that could indicate a cyberattack.