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

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

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 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: 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

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

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

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

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