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
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 Adaboost 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 (selfstudy)
Week: 10

Multi Linear Perceptron (continued)

Convolutional Neural Network

Walkthrough of a practical problem solved by CNN

Error Function