CS670 - Artificial Intelligence - Fall'2011

• 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, Knowledge and Reasoning, Logical Agents, Propositional Logic, First-Order Logic, Uncertain Knowledge and Reasoning, Quantifying Uncertainty, Probabilistic Reasoning, Learning, Statistical Learning Theory, Bayesian Learning, Decision Tree learning, Neural Networks, Genetic Algorithms, Reinforcement Learning, Perception, Pictorial Knowledge Representation, Search, Action, and Robotics.
  
• Prerequisite: CS 610 – Data Structures and Algorithms

• S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd edition, Prentice Hall, 2010.
• V. N. Vapnik, The Nature of Statistical Learning Theory, 2nd edition, Springer, 2000.
• T. M. Mitchell, Machine Learning, WCB/McGraw-Hill, 1997.
• 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 - A Comprehensive Foundation, 2nd edition, Prentice-Hall, 1999.
• Selected papers and handouts.

1. Introduction
• AI Fundamentals
• 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
3. Knowledge and Reasoning - Logical Agents
• Knowledge-Based Agents
• Logic, Propositional Logic
• Models, Semantics, Inference, Validity and Satisfiability
  
• Propositional Theorem Proving, Resolution, CNF
  
4. Knowledge and Reasoning - First-Order Logic
• FOL Syntax and Semantics
  
• FOL Sentences, Models, Interpretation
• FOL Quantification, Properties of Quantifiers
• FOL KBs, Deducing Hidden Properties
  
5. 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
  
6. Uncertain Knowledge and Reasoning - Quantifying Uncertainty
• Acting under Uncertainty
• Uncertainty and Probability
• Syntax and Semantics
• Inference by Enumeration, Normalization
  
• Independence, Conditional Independence, Bayes' Rule
  
7. Uncertain Knowledge and Reasoning - Probabilistic Reasoning
• Representing Knowledge in an Uncertain Domain
• Bayesian Networks
• Optimal Feature Representation Methods and Search
• Optimal Feature Classification Methods and Search
  
8. Learning - Theory of Learning
   
• General Learning Model
  
• Unsupervised Learning and Examples
  
• Supervised Learning and Examples
9. Learning -Probabilistic Models
   
• Statistical Learning Theory (STL)
• Structural Risk Minimization (SRM)
• Support Vector Machines (SVM)
• The EM Algorithm
  
10. Learning - Other Popular Models
    
• Bayesian Learning
• Decision Tree
• Neural Networks
• Genetic Algorithms
  
• Reinforcement Learning
11. Perception - Pictorial Knowledge Representation
• Digital Image Fundamentals
• Image Formation
• Digital Image Formats/Protocols (JPEG, PNG, TIFF, PGM, PPM)
• Digital Video Fundamentals (CAV; NTSC/PAL/SECAM; S-Video)
12. Perception - Search in Frequency Domain
• FT/FFT
• Lowpass and Highpass Filtering
• Convolution, Correlation, and Autocorrelation Theorems
• Pictorial Information Search using FFT Features
13. Perception - Search in Spatial Domain
• Geometric Feature Representation
• Edge Detection (Canny, Zero-crossing, LOG, Prewitt, etc.)
• Line and Curve Detection (Hough Transform)
• Pictorial Information Search using Geometric Features
14. Action - Robotics
• Sensors and Vision
• Path Planning
• Moving and Control
  

Midterm 30%

Final 30%

Projects and presentation (topics are related to our course Contents) 30%

Class attendance and participation 10%

NJIT Honor Code will be upheld, and any violations will be brought to the immediate attention of the Dean of Students

Students will be consulted with by the instructor and must agree to any modifications or deviations from the syllabus throughout the course of the semester.

• Berkeley AI Course Materials
• Stanford AI Course Materials
• Prolog:
  
• J.R. Fisher, The Prolog Tutorial
  
• Lisp:
  
• Paul Graham, ANSI Common Lisp, Prentice Hall, 1995.
• MATLAB
• Matlab Primer (Third Edition, By Kermit Sigmon - pdf file)
• Matlab Getting Started (copyright Mathworks - pdf file)
• Matlab Image Processing Toolbox User's Guide (copyright Mathworks - pdf file)