Week 01:
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Topic: Introduction, Intelligent Agents, & Review

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Objectives:

• Define intelligence from multiple perspectives
• Define and give examples of artificial intelligence from multiple perspectives
• Define rationality and a rational agent in the context of artificial intelligence
• Define, diagram, and give examples of the following intelligent agents:
  • Simple reflex agent
  • Model-based reflex agent
  • Goal-based agent
  • Utility-based agent
  • Learning agent
• Define and specify task environment characteristics for any given problem in artificial intelligence:
  • Fully Observable versus Partially Observable
  • Single Agent versus Multiagent
  • Deterministic versus Stochastic
  • Episodic versus Sequential
  • Static versus Dynamic
  • Discrete versus Continuous
  • Known versus Unknown

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1. Sign up for accounts on the following educational websites:
   • Udacity: https://www.udacity.com/
   • Coursera: https://www.coursera.org/
   • edX: https://www.edx.org/
   
   
2. Review C++ 1998/2003 (C++98/03), Generic Programming, & the Standard Library
   • Programming - Lecture slides
     • Ch17: Vector and Free Store
     • Ch18: Vectors and Arrays
     • Ch19: Vector, Template, and Exceptions
     • Ch20: Containers and Iterators
     • Ch21: Algorithms and Maps
   • Reference: Programming - Principles and Practice Using C++ by Bjarne Stroustrup
   
   
3. Introduction to C++ 2011 (C++11)
   • Note: g++ version 4.7.2 contains all of the C++11 features that we will use in this class. g++ version 4.7.2 can be used on linprog4.cs.fsu.edu and should be called using g++47 -std=c++11 to compile C++11 code.
   • Read about the following C++11 features that we will use in this class:
     1. auto
     2. decltype
     3. Lambda Functions
     4. Value Initialization, List Initialization, and Aggregate Initialization
     5. Range-based for loops
     6. nullptr
     7. Alias Templates
     8. <memory> Header (esp. std::shared_ptr and std::make_shared<>())
     9. std::pair
     10. Standard Library Containers (esp. the Container Adaptors: std::stack, std::queue, and std::priority_queue)
     11. <iterator> Header (esp. std::begin() and std::end())
     12. <algorithms> Header
     13. <cmath> Header
     14. <random> Header
     15. Lvalue versus Rvalue References, Move Constructors, Move Assignment Operators, std::move(), std::forward<>(), and Move Semantics (Part 1 and Part 2)
   • Marius Bancila: Ten C++11 Features Every C++ Developer Should Use
   • (Optional) Herb Sutter: Elements of Modern C++ Style
   • (Optional) Bjarne Stroustrup: A Tour of C++
     1. The Basics
     2. Abstraction Mechanisms
     3. Containers and Algorithms
     4. Concurrency and Utilities
   • (Optional) Bjarne Stroustrup: C++11 FAQ
   • Reference: C++ Standard (the C++ Standard Library is defined starting at Chapter 17)
   • Reference: C++ Primer, Fifth Edition by Stanley B. Lippman, Josee Lajoie, and Barbara E. Moo
   • Reference: C++11 Language Reference
   • Reference: C++11 Standard Library Reference
   • Reference: libstdc++ Module Reference
   • Reference: C++11 Support in GCC
   • Reference: C++11 Library Support in GCC
   • Reference: Standard C++
   
   
4. Math Review:
   • Do the following:
     1. Navigate to Coursera's Machine Learning course.
     2. Press the "Preview" button.
     3. Watch the following videos in the "III. Linear Algebra Review (Week 1, Optional)" section (61 min):
        • Matrices and Vectors (9 min)
        • Addition and Scalar Multiplication (7 min)
        • Matrix Vector Multiplication (14 min)
        • Matrix Matrix Multiplication (11 min)
        • Matrix Multiplication Properties (9 min)
        • Inverse and Transpose (11 min)
     Reference: Linear Algebra Review from Coursera's Machine Learning course by Andrew Ng.
     
     
   • Quickly review the following:
     
     Note: Some of the entries that follow are overly complex. Read the opening paragraphs, but do not dwell too long on the equations that follow.
     
     
     • Vector
     • Vector Addition
     • Vector Subtraction
     • Scalar Multiplication
     • Vector Multiplication
     • Dot Product
     • Normal Vector
     • Norm
     • Matrix
     • Matrix Addition
     • Matrix Multiplication
     • Transpose
     • Matrix Inverse
     • Determinant
     • Linear Equation
     • Math World: Level Set
     • Math Insight: Level Sets
     • Math Insight: Level Set Examples
     • Plane
     • Parabola
     • Variance
     • Sample Variance
     • Standard Deviation
     • Minimum
     • Sample Mean
     • Weighted Mean
     • Least Squares Fitting
     • Paraboloid
     • Covariance
     • Covariance Matrix
     • Symmetric Matrix
     • Eigenvalue
     • Eigenvector
     • Singular Value Decomposition
     • (Optional) Scale Matrix
     • (Optional) Rotation Matrix
     • (Optional) Rotation Matrix
     • (Optional) Principal Component Analysis
     • Distance
     • Euclidean distance
     • (Optional) Manhattan distance
     • (Optional) Chebyshev distance
     • (Optional) Minkowski distance
     • (Optional) Mahalanobis distance
     • Vector Norm
     • Circle
     • Ellipse
     • Ellipsoid
     • Taylor Series
     • Normal Distribution
     • (Optional) Multivariate Normal Distribution
     • Newton's Method
     • Gradient
     • Method of Steepest Descent
     • (Optional) Appendix A of Computer Vision: Algorithms and Applications by Richard Szeliski.
     
     
   • (Optional) Review Linear Algebra at Khan Academy:
     • Vectors and spaces
     • Matrix transformations
     • Alternate coordinate systems (bases)
   
   
5. Complete the "1. Welcome to AI" unit in the Introduction to Artificial Intelligence course from Udacity:
   1. Sign In to Udacity
   2. Select Introduction to Artificial Intelligence from the Course Catalog
   3. Press the "Take the Class" button
   4. Watch the videos and take the quizzes in the "1. Welcome to AI" unit.
   
   Alternatively, download the following ZIP file(s) containing Flash Video files:
   • 1. Welcome to AI
   
   Note: Peter Norvig is one of the instructors in these videos. He is also the author of our textbook.
   
   
6. Read the following pages from Artificial Intelligence: A Modern Approach, Third Edition (cited as AIMA):
   • Required: 42 Pages

     Order	Topic	Book	Section	Page Numbers	Pages	Presentations
     1.	Introduction	AIMA	Chapter 1 Intro:	Page 1	(1 Page)	Artificial Intelligence and Overview
     2.	What is AI?	AIMA	Section 1.1:	Pages 1–5	(5 Pages)	Artificial Intelligence and Overview
     3.	Intelligent Agents	AIMA	Chapter 2:	Pages 34–59	(26 Pages)	Intelligent Agents and Overview
     4.	Vectors, Matrices, and Linear Algebra	AIMA	Section A.2:	Pages 1055–1057	(3 Pages)
     5.	Probability Distributions	AIMA	Section A.3:	Pages 1057–1059	(3 Pages)
     6.	Local Search in Continuous Spaces	AIMA	Sections 4.2:	Pages 129, 131–133	(4 Pages)	Local Search Algorithms

     
     
   • Optional: 26 Pages

     Order	Topic	Book	Section	Page Numbers	Pages	Presentations
     1.	Foundations, History, and State of the Art	AIMA	Sections 1.2–1.5:	Pages 5–31	(26 Pages)	Artificial Intelligence and Overview

     
     
   Reference: University of California, Berkeley's CS 188: Introduction to Artificial Intelligence (Fall 2005) by Stuart Russell
   Reference: Stanford University's CS221: Artificial Intelligence: Principles and Techniques (Autumn 2012-2013) by Percy Liang
   
   
7. Study the following presentations:
   • Introduction to the Mathematics of Regression, Part 1:
     • Presentation:
       • PDF: MATHEMATICS_OF_REGRESSION_1.PDF
       • PPTX: MATHEMATICS_OF_REGRESSION_1.PPTX
     • Video:
       • MP4:
         • 1D Data (Mean, Variance, & Standard Deviation): MATHEMATICS_OF_REGRESSION_1_1.MP4
         • 2D Data (Covariance): MATHEMATICS_OF_REGRESSION_1_2.MP4
         • Linear Regression: MATHEMATICS_OF_REGRESSION_1_3.MP4
       • WMV:
         • 1D Data (Mean, Variance, & Standard Deviation): MATHEMATICS_OF_REGRESSION_1_1.WMV
         • 2D Data (Covariance): MATHEMATICS_OF_REGRESSION_1_2.WMV
         • Linear Regression: MATHEMATICS_OF_REGRESSION_1_3.WMV
   • Introduction to the Mathematics of Classification, Part 1:
     • Presentation:
       • PDF: MATHEMATICS_OF_CLASSIFICATION_1.PDF
       • PPTX: MATHEMATICS_OF_CLASSIFICATION_1.PPTX
     • Video:
       • MP4:
         • Classification using Level Set & Normal Vector: MATHEMATICS_OF_CLASSIFICATION_1_1.MP4
         • Rotation: MATHEMATICS_OF_CLASSIFICATION_1_2.MP4
         • Translation: MATHEMATICS_OF_CLASSIFICATION_1_3.MP4
       • WMV:
         • Classification using Level Set & Normal Vector: MATHEMATICS_OF_CLASSIFICATION_1_1.WMV
         • Rotation: MATHEMATICS_OF_CLASSIFICATION_1_2.WMV
         • Translation: MATHEMATICS_OF_CLASSIFICATION_1_3.WMV
   
   
8. Do Assignment 01
   
   
9. Bookmark the following websites:
   • videolectures.net: Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL)
   • Wolfram Alpha
   • Computer Vision: Algorithms and Applications by Richard Szeliski
   • The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition) by Trevor Hastie, Robert Tibshirani, & Jerome Friedman
   • Planning Algorithms by Steven M. LaValle