date | week | topic | slides + videos |
---|---|---|---|
19.09.2022 | 1 | Lec 1: Machine learning 101: model, loss, learning, issues, regression, classification | 01_intro_learning_regression_classification.pdf lec1_recording |
26.09.2022 | 2 | Lec 2: Under the hood of a linear classifier: two-class and multi-class linear classifier on RGB images | 02_classification.pdf lec2_recording |
03.10.2022 | 3 | Lec 3: Where the hell does the loss come from? MAP and ML estimate, KL divergence and losses. | 03_mle.pdf lec3_recording |
10.10.2022 | 4 | Lec 4: The story of the cat's brain surgery: fully-connected NN + fast backpropagation via Vector-Jacobian-Product (VJP), cortex + convolutional layer | 04b_convnets.pdf lec4_recording |
17.10.2022 | 5 | Lec 5: Under the hood of auto-differentiation: Vector-Jacobian-Product (VJP) vs chainrule and multiplication of Jacobians, convolutional layer and its VJP | 04a_fully_connected_neural_nets.pdf lec5_recording |
24.10.2022 | 6 | Midterm test | vir_2022_training_questions_midterm_test.pdf (“pure SGD” = “SGD”, i.e. momentum = 0) |
31.10.2022 | 7 | Lec 6: Why is learning prone to fail? - Structural issues: layers + issues, batch-norm, drop-out | layers.pdf lec6_recording |
07.11.2022 | 8 | Lec 7: Why is learning prone to fail? - Optim. issues: optimization vs learning, KL divergence, SGD, momentum, convergence rate, Adagrad, RMSProp, AdamOptimizer, diminishing/exploding gradient, oscillation, double descent | training.pdf lec7_recording |
14.11.2022 | 9 | Lec 8: What can('t) we do with a deep net?: Classification (ResNet, Squeeze and Excitation Nets), Segmentation (DeepLab), Detection (Yolo, fast-RCNN), Regression (OpenPose), Spatial Transformer Nets, Memory and attention (recurrent nets, Image transformers with attention module) | 08a_architectures_class_segm_simplified.pdf 08b_architectures_simple_reg_det_stn.pdf lec8_recording |
21.11.2022 | 10 | Lec 9: Reinforcement learning: Approximated Q-learning, DQN, DDPG, Derivation of the policy gradient (REINFORCE), A2C, Inverse RL, Applications, Lec 9 3/4:Learning to optimize: Backpropagation through unconstrained and constrained optimization problems, application (end-to-end differentiable modules cvxpy, gradSLAM, gradMPC, gradODE, pytorch3d) Lec 9 9/10:Self-supervision: (pseudo-labeling, privileged information, monodepth) | 09_reinforcement_learning.pdf lec9_recording |
28.11.2022 | 11 | Lec 10: Generative Adversial Networks: Guest lecture by David Coufal, ÚI AV ČR | lec10_recording vir2022_a.pdf |
05.12.2022 | 12 | Lec 11: Normalizing Flows: Guest lecture by David Coufal, ÚI AV ČR | lec11_recording vir2022_b.pdf |
12.12.2022 | 13 | Exam test | vir_2022_training_questions_exam_test.pdf |
09.01.2023 | 15 | Lec 12: Deep learning for satellite imagery: Guest lecture of Michal Reinstein (chief scientist in Spaceknow) |
The playlist of all lecture recordings is here.
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, | lec_13_memory_attention(pdf) lec_13_recording |
03.01.2022 | 15 | Lec 14: Exam Test ET |
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.pdfmle_01_regression.pdf mle_regression.py lec2_internal_viewerlec_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, | 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) |