Event	Date	Room	Description	Material*
Lecture 1	Monday Nov 18	B12D i-51 (64)	Introduction Research field. Neuroscience, computer vision and machine learning background. Modern deep learning. About this course.	[slides]
Lecture 2	Wednesday Nov 20	B12D i-60 (44)	Visual representation Global/local visual descriptors, dense/sparse representation, local feature detectors. Encoding/pooling, vocabularies, bag-of-words. VLAD*, Fisher vectors*, embeddings*. HMAX.	[slides]
Lecture 3	Monday Nov 26	Jersey	Local features and spatial matching Derivatives, scale space and scale selection. Edges, blobs, corners/junctions. Dense optical flow* / sparse feature tracking*. Wide-baseline matching. Geometric models, RANSAC, Hough transform. Fast spatial matching*.	[slides]
Lecture 4	Wednesday Nov 28	B12D i-58 (44)	Codebooks and kernels Geometry/appearance matching. Bag-of-words. k-means clustering, hierarchical*, approximate*, vocabulary tree*. Soft assignment, max pooling*. Match kernels, Hamming embedding, ASMK*. Pyramid matching, spatial pyramids. Hough pyramids*.	[slides]
Lecture 5	Monday Dec 2	B12D i-51 (64)	Learning Binary classification. Perceptron, support vector machines, logistic regression. Gradient descent, regularization, loss functions, unified model. Multi-class classification. Linear regression*, basis functions. Neural networks, activation functions.	[slides]
Lecture 6	Wednesday Dec 4	B12D i-51 (64)	Differentiation Stochastic gradient descent. Numerical gradient approximation. Function decomposition, chain rule, analytical gradient computation, back-propagation. Chaining, splitting and sharing. Common forward and backward flow patterns. Dynamic automatic differentiation.	[slides][pynet]
Lecture 7	Wednesday Dec 11	B12D i-59 (44)	Convolution and network architectures Convolution, cross-correlation, linearity, equivariance, weight sharing. Feature maps, matrix multiplication, 1x1 convolution. Padded*, strided*, dilated* convolution. Pooling and invariance. Convolutional networks: LeNet-5, AlexNet, ZFNet*, VGG, NiN*, GoogLeNet.	[slides]
Milestone	Sunday Dec 15		Oral presentation Selection of papers to present.	[instructions]
Lecture 8	Monday Dec 16	B12D i-60 (44)	Optimization and deeper architectures Optimizers: momentum, RMSprop, Adam, second-order*. Initialization: Gaussian matrices, unit variance, orthogonal*, data-dependent*. Normalization: input, batch, layer*, weight*. Deeper networks: residual, identity mappings*, stochastic depth*, densely connected.	[slides]
Lecture 9	Monday Dec 18	B12D i-60 (44)	Object detection Background: Viola and Jones, DPM, ISM, ESS*, object proposals, non-maximum suppression. Two-stage: R-CNN, SPP, fast/faster R-CNN, RPN. Bounding box regression*. Part-based*: R-FCN, spatial transformers, deformable convolution. Upsampling: FCN, feature pyramids. One-stage: OverFeat, YOLO, SSD*, RetinaNet, focal loss.	[slides]
Lecture 10	Monday Jan 6	B12D i-59 (44)	Retrieval Local vs. global descriptors for visual search. Pooling from CNN representations: MAC, R-MAC, SPoC*, CroW*. Manifold learning, siamese and triplet architectures. Fine-tuning with constrastive or triplet loss, learning to rank. Graph-based methods, diffusion, unsupervised fine-tuning.	[slides]
Evaluation 1	Monday Jan 13	B12D i-59 (44)	Written exam
Evaluation 2	Monday Jan 20	B12D i-59 (44)	Oral presentations	[instructions]

^*All material licensed CC BY-SA 4.0

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