# AI is Math

Check out my course site: AIisMath.com

This is my CV course raw data git repo - you can see the raw .py/ .ppt files here.

## Course TOC

### Filtering and resampling(slides)

• Noise and filtering (notebook)
• Frequency representation
• Decimation
• Interpolation

### Image formation(slides)

• BRDF
• Pinhole camera
• Digital camera
• The human eye

### Geometric transformation(slides)

• 2D->2D transformations (notebook)
• 3D->3D transformations
• 3D->2D transformations (3D projections)
• Perspective projection
• Orthographic projection

### Camera calibration(slides)

• What is camera calibration?
• Camera extrinsics
• Perspective projection
• Camera intrinsics
• Full camera matrix
• Calibration methods and distortions (notebook)

### Features(slides)

• What and why we need features detection?
• Feature detection
• Feature description
• Template matching
• HOG
• SIFT descriptor
• SIFT feature matching (SIFT notebook)
• Panoramas

### Stereo(slides)

• Structure from motion
• Triangulation
• Stereo matching
• Camera rectification
• Epipolar geometry
• Essential matrix
• Fundamental matrix
• Estimating the fundamental matrix
• Other 3D sensors

### Neural networks basics(slides)

• The classification problem- again
• NN history
• Perceptron
• Hyperplanes
• Activation
• Dense layer
• Multi-layer perceptron (MLP)
• Optimization
• Softmax + cross entropy + loss
• Basic data preprocessing
• Data normalization
• Train, validation and test splits
• Fully connected net

### Neural networks 2(slides)

• ConvNets
• Convolution layer
• Pooling layer
• Overfitting
• Architectures
• Alexnet (dropout)
• VGG
• ResNet (batch norm)

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Slides (13,480
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