Week 1: Course introduction, Introduction to deep learning, Introduction to neuron
Week 2: Multilayer perceptron (MLP), Gradient descent, Backpropagation in MLP
Week 3: Optimization and regularization, Regularization and preprocessing, Convolutional neural network (CNN)
Week 4: CNN properties, CNN architectures, Introduction to recurrent neural network (RNN), Encoder-Decoder models in RNN
Week 5: Low-level vision, Spatial and frequency domain filtering, Edge detection
Week 6: Line detection, Feature detectors, Harris corner detector
Week 7: Blob detection, SIFT, Feature descriptors, SURF
Week 8: Single-view geometry, 2D Geometric transformations, Camera intrinsics and extrinsics
Week 9: Two-view stereo, Algebraic representation of epipolar geometry, Fundamental matrix computation
Week 10: Structure from motion, Batch processing in SFM, Dense 3D reconstruction
Week 11: Deepnets for stereo and SFM, Mid-level vision, Image segmentation
Week 12: Deepnets for segmentation, High-level vision, Deepnets for object detection
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