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Applied Machine Learning

Intensive training for a career in computer vision and machine learning.

Week: 1
 
  • Basic Image/Video processing

    • Pixel, Image and Video

    • Color space:

      • RGB

      • Gray 

      • LAB

      • HSV

    • Segmentation:

      • Thresholding

      • Binary image
         

  • Install OpenCV, setup OpenCV for Visual Studio

  • Code some of the examples from the above lecture

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Week: 3
  • Feature extraction / classification:

    • Convolution kernel (continued)

    • Edge detection filters

      • Canny

      • Sobel

      • Gabor

      • Laplacian

  • Code some of the examples from the above lecture

Week: 5
  • Feature extraction / classification:​

    • Local Binary Pattern

    • Hough Transform

  • Code some of the examples from the above lecture

Week: 7
  • KNN and Clustering

    • ​K nearest neighbour

    • K mean clustering

    • Hierarchal Clustering

    • Error Function

  • Support vector machine

Week: 9
  • Famous Ada-boost and cascading classifier​

    • Walkthrough of a complete algorithm, explanation of a classifier for object detection

    • Perceptron 

    • Linear Perceptron

    • Multi Linear Perceptron

    • Error Function

Week: 2
 
  • Image processing / Feature extraction

    • Segmentation (continued)

      • Use binary image for segmentation of an object

    • Fourier transforms

    • Convolution kernel 

      • Blurring

  • Code some of the examples from the above lecture

Week: 4
  • Feature extraction / classification:

    • Gray Level Cooccurrence

    • Matrix Haar Feature

  • Code some of the examples from the above lecture

Week: 6
  • What is Machine learning/Data Science?​

    • Supervised vs unsupervised learning

    • Statistics, advance mathematics and computing

    • Training, testing and validation data

    • Confusion matrix

Week: 8
  • Decision tree

    • ​ID3 algorithm

    • Entropy

    • Information gain

  • Error Function

  • Naïve Bayes classifier (self-study)

Week: 10
  • Multi Linear Perceptron (continued)

  • Convolutional Neural Network

  • Walkthrough of a practical problem solved by CNN

  • Error Function