L1. Least Mean Squares Line Fitting
     ( lab_01e.pdf , points_LeastSquares.zip , Intro_OpenCV.pdf , OpenCV Application )

L2. RANSAC Line Fitting
     ( lab_02e.pdf , points_RANSAC.zip )

L3. Hough Transform Line Fitting
      ( lab_03e.pdf , images_Hough.zip )

L4. Distance Transform
      ( lab_04e.pdf , images_DT_PM.zip )

L5. Statistical Data Analysis
      ( lab_05e.pdf, images_faces.zip )

L6. Principal Component Analysis
     ( lab_06e.pdf, data_PCA.zip )

L7. K-means Clustering
     ( lab_07e.pdf, images_Kmeans.zip )

L8. K-nearest Neighbor Classifier
     ( lab_08e.pdf, images_KNN.zip )

L9. Naïve Bayes Classifier
      ( lab_09e.pdf , images_Bayes.zip )

L10. Linear Classifiers and the Perceptron Algorithm
      ( lab_10e.pdf , images_Perceptron.zip )

L11. AdaBoost with Decision Stumps
      ( lab_11e.pdf , images_AdaBoost.zip )

L12. Support Vector Machine
      ( lab_12e.pdf , svm_images.zip , TestSVM.zip )

L13. Laboratories recovery (absences)

List of projects
Making documentation
Agenda

Discussions regarding projects elaboration

Projects' assignment

Discussions about the theoretical background study

Theoretical background - presentation deadline (PPT slides)

Design - presentation deadline (PPT slides)

Implementation - discussions

Implementation - presentation deadline (current implemetation)

Implementation - application improvements

Final presentation - application and documentation