Introduction to Learning and Perception Introduction Introduction Cameras and Eyes Images and Arrays Image Processing Linear Filters Gradient Filters Anisotropic Filters Frequency Domain Fourier Transform Image Compression Nonlinear FIlters Document Normalization Binary Morphology Grayscale Morphology Grayscale Morphology in 1D Distance Transform Labeling Template Matching Learning Classification Nearest Neighbor Simple Probabilities Bayesian Decision Theory Parametric Gaussian Classifier Perceptrons Linear Least Squares Logistic Regression Using Linear Classifiers for Nonlinear Classification k-Means Simple PCA Summary of Classifiers Applications Speech Spectrum Dynamic Time Warping Markov Models OCR by HMM Appendix Introduction to Python Quadratics Normal Distributions Bayesian Parameter Estimation [Frequency Modulation](http://nbviewer.ipython.org/urls/bitbucket.org/tmbdev/teaching-lw/raw/tip/Frequency Modulation and Encoding.ipynb) Homework Edge Detection Homework Ferquency Modulation