Midterm Exam

The Midterm Exam may be taken anytime from 24 JUN 2013 (Monday) to 01 JUL 2013 (Monday).

1. Study the following Assignment Problems:
   • Assignment 01, Written Problem 1: Terminology
   • Assignment 01, Written Problem 3: Linear Regression
   • Assignment 01, Written Problem 4: Classification using a Level Set/Normal Vector
   • Assignment 04, Written Problem 3: Neural Networks (esp. the weights and the part of the classification where level sets/normal vectors are used)
   • Assignment 02, Written Problem 5: Naïve Bayes Network
   • Assignment 03, Written Problem 1: Parametric vs. Nonparametric Methods
   • Assignment 05, Written Problem 2: A* Search
   • The similarities and differences between Assignment 01, Programming Problem 1: Linear Regression and Assignment 03, Programming Problem 1: Logistic Regression
2. Read the following:
   • A Few Useful Things to Know about Machine Learning by Pedro Domingos
3. Memorize the following Algorithms:
   • Algorithms for the Midterm Exam
4. (Optional) For a review of the regression and classification algorithms, 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
   
   
5. Note: Questions derived from the following sources:
   • Artificial Intelligence: A Modern Approach, Third Edition by Stuart Russell and Peter Norvig.
   • Udacity's Introduction to Artificial Intelligence course by Peter Norvig and Sebastian Thrun.
   • Coursera's Machine Learning course by Andrew Ng.
   • Coursera's Neural Networks for Machine Learning course by Geoffrey Hinton.
   • Coursera's Machine Learning course by Pedro Domingos.
   • edX's CS188.1x Artificial Intelligence course from BerkeleyX by Dan Klein.
   • Coursera's Introduction to Logic course by Michael Genesereth.
   • NPTEL's Artificial Intelligence course by Pallab Dasgupta.
   • NPTEL's Artificial Intelligence course by Anupam Basu and Sudeshna Sarkar.