New Jersey Institute of Technology
Department of Computer Science
CS370 - Introduction to Artificial
Intelligence - Spring'2016
Monday 2:30
- 5:25PM, KUPF 203
Course
Description | Goals | Outcomes
| Readings
| Tentative Contents | Grading Policy | Miscellaneous
Chengjun Liu, Ph.D.
Phone: 973-596-5280
Email: chengjun.liu@njit.edu
Office: GITC 4306
Office Hours: Monday 1:25-2:25PM, Tuesday 3:30-5:30PM, or by appointment
Course Description
- 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, Search, Knowledge and Reasoning,
Logical Agents, First-Order Logic and Inference, Uncertain
Knowledge and Reasoning, Quantifying Uncertainty, Probabilistic
Reasoning, Perception, Pictorial Knowledge Representation, and
Search in Frequency and Spatial Domains. Additional topics
include Machine Learning, Neural Computation, Evolutionary
Computation, and Robotics.
- Prerequisites: CS 114 and (Math 226 or
CS 241)
Specific
Goals for the Course
- Students are prepared to work on AI related projects, such as
problem solving, knowledge representation and reasoning,
perception, and search.
- Students learn the introductory concepts and methodologies for
artificial intelligence.
- Students learn the fundamental materials with a broad scope of
applications.
Measurable Learning Outcomes
- 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, Search, Knowledge and Reasoning, Logical
Agents, First-Order Logic and Inference, Perception, Pictorial
Knowledge Representation, and Search in Frequency and Spatial
Domains.
Readings
- S. Russell and P. Norvig, Artificial
Intelligence: A Modern Approach, 3rd edition, Prentice Hall, 2010.
- Selected papers and handouts.
Tentative
Contents
- Introduction
- AI Fundamentals: Knowledge &
Search, Cognitive Science, Turing Test, Ancient Philosophers
(Logic)
- Programming Languages: Lisp, Prolog, C/C++, Java, Matlab
- Related Fields: Machine Learning,
Neural Networks, Evolutionary Computation, Computer Vision
- AI Basic Concepts
- Neural Networks, Connectionism
- Expert Systems, Symbolism
- AI winters
- Agents, Acting/Thinking
Humanly/Rationally
- Problem Solving
- Intelligent Agent: Sensors, Actuators,
Agent Program
- Rational Agents: Vacuum-cleaner Agent
- PEAS
- Solving Problems by Searching: problem-solving agent
- Search
- Blind Search Strategies vs. Informed Search Strategies
- Breadth-first Search, Depth-first Search
- Greedy Best-first Search
- A* Search (completeness, optimality, complexity)
- Knowledge and Reasoning - Propositional Logic and Logical Agents
- Knowledge Base, Models, and Knowledge-Based Agents
- Propositional Logic Knowledge Representation Language
- Syntax and Semantics
- Knowledge and Reasoning - Logical
Reasoning
- Logical Reasoning: Entailment and Inference (soundness,
completeness)
- Propositional Theorem Proving:
Validity, Satisfiability, Reduction to
Absurd
- MP Inference Rule, Resolution Inference Rule, Horn Form, CNF
- Knowledge and Reasoning - First-Order Logic
- Propositional Logic vs. First-Order Logic: objects,
relations (unary, n-ary), functions
- First-Order Logic: Syntax and Semantics (predicates,
variables, quantifiers)
- First-Order Logic Knowledge
Representation Language, Model, Interpretation
- Knowledge and Reasoning - Inference in First-Order Logic
- Universal Instantiation, Existential Instantiation
- Substitution and Unification
- Generalized MP Rule, Soundness of GMP
- Resolution Inference Rule, CNF
- 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)
- Perception - Search in Frequency Domain
- FT/FFT
- Lowpass and Highpass Filtering
- Convolution, Correlation, and
Autocorrelation Theorems
- Pictorial Information Search using FFT
Features
- 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
- Learning
- Inductive Learning
- Decision Tree Learning
- Neural Networks Learning
- Evolutionary Computation (optional)
- Genetic Algorithms (GA)
- Evolutionary Strategy (ES)
- Evolutionary Programming (EP)
- Robotics (optional)
- Sensors and Vision
- Path Planning
- Moving and Control
Grading Policy
Homework 20%
Midterm exam 20%
Project and presentation (topics are related to our course Contents) 20%
Class attendance and participation 10%
Final exam 30%
Academic integrity and honesty are of
paramount importance. NJIT Honor Code will be upheld, and any
violations will be brought to the immediate attention of the Dean
of Students.
Miscellaneous