This course is in detail in computer vision. Topics include camera models, multi-view geometry, reconstruction, some low-level image processing, and high-level vision tasks like image classification and object detection.

Here is a rough outline of topics:

• Getting started with Images
• Image formation 
• Drawing Functions
• Practical linear algebra 
• Image processing / descriptors
• Image warping 
• Feature Detection
• Feature Extraction
• Feature Segmentation
• Linear models + optimization 
• Neural networks
• Applications of neural networks
• Motion and flow 
• Epi Polar Geometry
• Applications 
• Array Manipulation: Homework assignments will involve manipulating multidimensional arrays using numpy and Pytorch. Some prior exposure to either of these frameworks will be useful, but if you’ve never used them before then the first homework assignment will help get you up to speed.
• Linear Algebra: In addition to basic matrix and vector operations, it will be good to know least squares, Eigen- and singular-value decompositions.
• Calculus: Will extend suitable training and make you comfortable with the chain rule, and taking gradients and partial derivatives of vector-valued functions.

• Much of modern computer vision involves applying ideas from linear algebra to real-world data, and doing so often requires doing calculus with vector-valued functions.