CS2109S: Introduction to Artificial Intelligence

This course introduces basic concepts in Artificial Intelligence (AI) and Machine Learning (ML). It adopts the perspective that planning, games, and learning are related types of search problems, and examines the underlying issues, challenges and techniques. Planning/games related topics include tree/graph search, A* search, local search, and adversarial search (e.g., games). Learning related topics include supervised and unsupervised learning, model validation, and neural networks.

Topics:

• Intelligent Agents
• “Classical” AI (Search)
  • Uninformed Search:
    • Breadth-First-Search (BFS)
    • Uniform-Cost-Search (UCS)
    • Depth-limited search (DLS)
    • Iterative Deepening Search (IDS)
  • Informed Search:
    • Greedy best-first search
    • A-Star search
    • Heuristics
  • Local Search:
    • Hill-Climbing Search
    • Simulated Annealing
    • Evaluation functions for local search strategies
  • Adversarial Search:
    • Minimax
    • Alpha-Beta Pruning
    • Evaluation functions for game scenarios

• Classical Machine Learning
  • Supervised Learning
    • Decision Tree
      • Decision Trees
      • Ensemble Methods (e.g., Random Forests, Gradient Boosting)
    • Linear Regression:
      • Linear Regression
      • Logistic Regression
      • Gradient Descent
      • Normal Equation
      • Regularization
    • Support Vector Machines and Kernel Methods:
      • Support Vector Machines
      • Kernel Methods
  • Unsupervised Learning:
    • K-Means Clustering
    • Principal Component Analysis (PCA)

• “Modern” Machine Learning
  • Neural Networks:
    • Multi-Layer Perceptrons (MLP)
    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN)
    • Attention Mechanisms
    • Transformers