Week | Topics | Study Materials | Materials |
1 |
Overview of Digital Image Processing applications
Introduction to Machine Learning
|
|
|
2 |
Image sampling and quantization, Relation of the pixels
Linear Regression with One Variable
|
|
|
3 |
Intensity transformations, histogram processing, spatial filters
Linear Regression with Multiple Variables
|
|
|
4 |
Fourier transform of sampled functions, Discrete Fourier Transform (DFT) and properties of 2D DF
Logistic Regression
|
|
|
5 |
Filtering in frequency domain
Regularization
|
|
|
6 |
Filtering in frequency domain (Continued)
Neural Networks
|
|
|
7 |
Image restoration and reconstruction
Machine Learning System Design
|
|
|
8 |
Image restoration and reconstruction (Continued)
Support Vector Machines
|
|
|
9 |
Morphological operations
Unsupervised Learning
|
|
|
10 |
Morphological operations (Continued)
Dimensionality Reduction
|
|
|
11 |
Good project management practices
|
|
|
12 |
Group Based Project Supervision: Feedbacks and Troubleshooting
|
|
|
13 |
Group Based Project Supervision: Feedbacks and Troubleshooting
|
|
|
14 |
Group Based Project Supervision: Feedbacks and Troubleshooting
|
|
|
15 |
|
|
|
16 |
|
|
|