CS670 - Artificial Intelligence - Fall'2025

• This course introduces concepts, approaches and techniques of artificial intelligence, and focuses on materials that are fundamental and have a broad scope of applications.  Topics include Problem Solving, Intelligent Agents, Logical Agents, Propositional Logic Knowledge Representation and Reasoning, First-Order Logic Knowledge Representation and Reasoning, Uncertain Knowledge Representation and Reasoning, Quantifying Uncertainty, Probabilistic Reasoning, Learning, Statistical Learning Theory, Bayesian Learning, Decision Tree learning, Neural Networks, Deep Learning, and Perception.

• Students learn the concepts, approaches and techniques of artificial intelligence.
  
• Students learn the materials that are fundamental and have a broad scope of applications in artificial intelligence, such as Problem Solving, Intelligent Agents, Knowledge and Reasoning, Logical Agents, Propositional Logic, First-Order Logic, Uncertain Knowledge and Reasoning, Quantifying Uncertainty, Probabilistic Reasoning, Learning, General Learning Model, Decision Tree learning, Unsupervised Learning, Supervised Learning, Statistical Learning Theory, Structural Risk Minimization, Support Vector Machine, Perception.
  

• S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th edition, Prentice Hall, 2020.
• V. N. Vapnik, The Nature of Statistical Learning Theory, 2nd edition, Springer, 2000.
• C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
  
• K. A. De Jong, Evolutionary Computation: A Unified Approach, The MIT Press, 2006.
• S. Haykin, Neural Networks and Learning Machines, 3rd Edition, 2009.
  
• Selected papers and handouts.

1. Introduction
• AI Fundamentals (Turing test, cognitive science, logic, learning, games, robot, vehicle, agent)
• AI prehistory and AI history: connectionism, symbolism, AI winters
  
• Programming Languages: Lisp, Prolog, Matlab, C/C++, Java
  
• Related Fields: ML, NN, EC, CV, PR, IP
  
2. Problem Solving
   
• Intelligent Agents
• Solving Problems by Searching
• Breadth-first Search, Depth-first Search
• Best-first Search, Greedy Search, A* Search
• Games (Adversarial Search, Alpha-Beta Pruning)
  
3. Learning - Theory of Learning
   
• General Learning Model
• Inductive Learning
  
• Learning Decision Trees
• Artificial Neural Networks (Perceptrons, RBF, Deep Learning)
  
4. Learning - Unsupervised Learning
• Clustering, K-Means, EM Algorithm
  
• Principal Component Analysis
• Applications: Compression, Feature Representation
  
5. Learning - Supervised Learning
• Bayes Classifier, Bayes Decision Rules
  
• Discriminant Analysis
• Applications: Feature Extraction for Classification
  
6. Learning -Probabilistic Models
   
• Statistical Learning Theory (STL)
• Structural Risk Minimization (SRM)
• Support Vector Machines (SVM)
7. Learning - Other Popular Models (optional)
   
• Bayesian Learning
• Genetic Algorithms
  
• Reinforcement Learning
8. Knowledge and Reasoning - Logical Agents
• Knowledge-Based Agents
• Logic, Propositional Logic
• Models, Semantics, Inference, Validity and Satisfiability
  
• Propositional Theorem Proving, Resolution, CNF
• Games (Wumpus World)
9. Knowledge and Reasoning - First-Order Logic
• FOL Syntax and Semantics
  
• FOL Sentences, Models, Interpretation
• FOL Quantification, Properties of Quantifiers
• FOL KBs, Deducing Hidden Properties
  
10. Knowledge and Reasoning - Inference in First-Order Logic
    
• Propositional vs. First-Order Inference
• Universal and Existential Instantiation
  
• Unification, GMP, Soundness of GMP
  
• FOL KB and Resolution
• Logic Programming - Prolog
  
11. Uncertain Knowledge and Reasoning - Quantifying Uncertainty
• Acting under Uncertainty
• Uncertainty and Probability
• Syntax and Semantics
• Inference by Enumeration, Normalization
  
12. Uncertain Knowledge and Reasoning - Probabilistic Reasoning (optional)
• Bayesian Networks
• Hidden Markov Models
• Kalman Filters
13. Perception - Search in Spatial Domain and Frequency Domain (optional)
• FFT, Lowpass and Highpass Filtering, Convolution Theorem
• Edge Detection, Line and Curve Detection (Hough Transform)
• Pictorial Information Search using Geometric or Frequency Features
14. Action - Robotics (optional)
• Sensors and Vision
• Path Planning
• Moving and Control

Homework 20%

Midterm exam 20%

Project and Presentation (topics are related to our course Contents) 20%

Class attendance and participation 10%

Final exam 30%

• Berkeley AI Course Materials
• Prolog:
  
• J.R. Fisher, The Prolog Tutorial
  
• Lisp:
  
• Paul Graham, ANSI Common Lisp, Prentice Hall, 1995.