Artificial Intelligence (CS221) - Lecture Notes

Jul 17, 2024

Artificial Intelligence (CS221) - Lecture Notes

Welcome to CS221: Artificial Intelligence. This course is taught by Percy and Dorsa, with a diverse team of teaching assistants (TAs).

Course Overview

  • Focus on artificial intelligence, both theoretical and applied aspects
  • Topics covered will range from the history of AI to technical paradigms in AI like modeling, inference, and learning
  • Students are encouraged to work on projects leveraging the skills and knowledge gained during the course

Introduction to Instructors and TAs

  • Percy: One of the instructors, emphasized the importance of AI and its current relevance in society
  • Dorsa: Co-teaching, specializes in robotics and robotic interactions
  • Teaching Assistants (TAs): Introduced themselves with their interests, spanning natural language processing, machine learning, data mining, computer vision

Key Announcements

  • Weekly sections to cover review and advanced topics (starts Thursday)
  • Homework 1 is posted and due next Tuesday at 11:00 PM, submit via Gradescope
  • Gradescope code to be posted on Piazza

History of AI

  • Dartmouth Workshop (1956): Foundational meeting organized by John McCarthy; laid groundwork for AI
    • Early optimism in AI with programs playing checkers, proving theorems, etc.
    • First AI Winter due to over-promising and limited computational power
  • 1970s-80s: Focus on knowledge-based systems with expert systems; second AI Winter due to manual effort and complexity
  • Deep Learning (1943-Present): Renewed interest in neural networks and backpropagation leading to modern successes like AlphaGo

AI Paradigms

  • AI as Agents: Focus on replicating human intelligence and capabilities
    • Areas of interest include visual perception, motor skills, language, and learning from experience
  • AI as Tools: Technology as a means to benefit society rather than replicating human intelligence
    • Applications include computer vision for poverty prediction, energy-saving algorithms, and addressing biases and fairness in systems

Modeling, Inference, and Learning Paradigm

  • Modeling: Simplifying real-world problems into mathematical models
    • Example: Representing a city as a graph for navigation
  • Inference: Asking questions about the model and solving problems mathematically
    • Example: Finding the shortest path in a graph
  • Learning: Fitting model parameters from data
    • Example: Assigning costs to edges in a graph based on travel data

Course Topics

  • Machine Learning: Building blocks of AI; focuses on transforming data into models
    • Central tenet: Moving complexity from code to data
  • Reflex Models: Simple models like linear classifiers and neural networks
    • Instantaneous processing without foresight
  • State-based Models: Handling planning and foresight in tasks
    • Includes search problems, Markov Decision Processes (MDPs), and adversarial games
  • Variable-based Models: Capturing dependencies among variables
    • Includes constraint satisfaction problems and Bayesian networks
  • Logic-based Models: High-level reasoning with diverse information
    • Examples include systems understanding and answering questions based on logic

Course Logistics

  • Prerequisites: Programming, discrete math, and probability
  • Coursework: Eight homeworks, written and programming, covering specific applications
    • One exam testing problem-solving and application of course material
  • Final Project: Group project with milestones; open-ended problems applying course concepts
    • Encouraged to work in groups of three with CA mentorship
  • Policies: Submission on Gradescope, use of Piazza for communication, adherence to Honor Code

Optimization in AI

  • Discrete Optimization: Using dynamic programming for complex problems
    • Example: Computing edit distance between strings
  • Continuous Optimization: Using gradient descent for problems like regression
    • Example: Fitting a line to data points by minimizing error

Summary

  • AI Paradigms: Focus on agents and tools, addressing challenges like security and bias
  • Technical Paradigms: Modeling, inference, and learning as a structured approach
  • Optimization: Key methods for solving AI problems in practice

Next Steps

  • Machine Learning: Deep dive starting next lecture