Lectures

date	week	topic	slides + videos
20.09.2021	1	Lec 1: MLE regression: derivation of L2 loss, prior	lec_01_MLE_regression
27.09.2021	2	Lec 2: MLE classifier: derivation of cross-entropy loss and logistic loss and linear classifier	lec_02_MLE_classifiction(pdf) lec_02_recording codes
04.10.2021	3	Lec 3: Fully-connected network: computational graph + backpropagation	lec_03_NN(pdf) lec_03_recording
11.10.2021	4	Lec 4: ConvNet: convolutional layer + backpropagation	lec_04_ConvNets(pdf) lec_04_recording
18.10.2021	5	Lec 5: Training: SGD, momentum, convergence rate, Adagrad, RMSProp, AdamOptimizer, diminishing/exploding gradient, oscilation	lec_05_training(pdf) lec_05_recording
25.10.2021	6	Lec 6: Layers: Convolution, Activation functions, Batch-Instance Norm, MaxPooling, Losses + backpropagation Test T1	lec_06_ConvNets training_examples.zip
01.11.2021	7	Lec 7: Architectures I: classification (ResNet, Squeeze and `excitation Nets)	lec_07_classification_segmentation(pdf) lec_07_recording
08.11.2021	8	Lec 8: Architectures II: segmentation (DeepLab), pose regression (OpenPose),	lec_08_classification_segmentation(pdf) lec_08_recording
15.11.2021	9	Lec 9: Architectures III: detection (Yolo), depth regression, spatial transformer nets	lec_09_regression_detection_stn(pdf) SVTI streams and recordings
22.11.2021	10	Lec 10: Reinforcement Learning I: Approximated Q-learning, DQN, DDPG, Policy gradient (REINFORCE),	lec_10_DQN_DDPG_REINFORCE(pdf) lec_10_recording
29.11.2021	11	Lec 11: Generative Adversarial Networks: guest lecture by David Coufal, ÚI AV ČR)	lect_11_GANs(pdf) lec_11_recording
06.12.2021	12	Lec 12: Reinforcement Learning II: Derivation of the policy gradient, A2C, Inverse Reinforcement Learning, Applications, end-to-end differentiable modules (cvxpy, gradSLAM, pytorch3d)	lec_12_A2C_IRL(pdf) lec_12_recording
13.12.2021	13	Lec 13: Memory and attention: Recurrent nets, Image transformers with attention module, ~~Depth completition (monodepth), Graph convolution,~~	lec_13_memory_attention(pdf) lec_13_recording
03.01.2022	15	Lec 14: Exam Test ET

All SVTI streams

Old lectures

date	week	topic	slides+videos+codes
21.09.2020	1	Lec 1: Regression/Classification as MLE: derivation of L2-loss, cross-entropy loss, logistic loss Lec 2: Neural networks: Fully-connected layer + computational graph + backpropagation	lec_1_video (incorrect audio sync!) vir_outline.pdf mle_01_regression.pdf mle_regression.py lec2_internal_viewer lec_2_video mle_02_classification.pdf mle_linear_classifier.py
28.9.2020	2	State holidays
05.10.2020	3	Lec 3: ConvNet: convolutional layer + backpropagation	lec3_internal_viewer lec3.video neural_nets.pdf, convnets.pdf convolution_codes.zip
12.10.2020	4	Lec 4: Training: SGD, momentum, convergence rate, Adagrad, RMSProp, AdamOptimizer, diminishing/exploding gradient, oscilation	lec4_internal_viewer lec4_video training.pdf (corrected slides)
19.10.2020	5	Lec 5: Layers: Convolution, Activation functions, Batch-Instance Norm, MaxPooling, Losses + backpropagation	lec5_internal_viewer, layers.pdf (corrected)
26.10.2020	6	Lec 6: Architectures I: classification (ResNet), segmentation (DeepLab) Test T1	lec6_internal_viewer architectures_i.pdf
02.11.2020	7	Lec 7: Learning from unlabelled data: Self-supervision: Contrastive learning, rotation, jigsaw, Colorization Weak-supervision: Multiple-instance learning, physical constraints; guest lecture by Patrik Vacek	lec7_internal_viewer unlabelled_data.pdf
09.11.2020	8	Lec 8: Architectures II: pose regression, detection (Yolo), depth regression, spatial transformer nets, LIFTs	lec8_internal_viewer architectures_ii.pdf
16.11.2020	9	Lec 9: Structured inputs: Recurrent neural networks, Convolution in 1,2 and 3D and other structures: guest lecture by Teymur Azayev	lec9_internal_viewer lecture_9_structure.pdf
23.11.2020	10	Lec 10: Reinforcement Learning I: DQN, GAE+TD(lambda), DDPG	lec10_internal_viewer reinforcement_learning.pdf
30.11.2020	11	Lec 11: Reinforcement Learning II: Policy gradients (REINFORCE), Inverse Reinforcement Learning, Applications, What did not fit to lectures: domain transfer, MAML, monodepth, depth from symmetries, Pytorch3D, CvxPyLayer (it did not fit even here )	lec11_internal_viewer reinforcement_learning.pdf
07.12.2020	12	Lec 12: Generative Adversarial Networks: guest lecture by David Coufal, ÚI AV ČR)	lec12_internal_viewer gans_coufal_vir_2019
14.12.2020	13	Exam Test ET	see BRUTE for results
04.01.2021	14	Selected teams present their semestral works (the rest presents during labs)