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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024
UPOZORNĚNÍ: Jsou dostupné studijní plány pro následující akademický rok.

Robot Learning

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Code Completion Credits Range Language
B3B33UROB Z,ZK 6 2P+2C Czech
Vztahy:
It is not possible to register for the course B3B33UROB if the student is concurrently registered for or has already completed the course B3B33VIR (mutually exclusive courses).
During a review of study plans, the course B3B33VIR can be substituted for the course B3B33UROB.
It is not possible to register for the course B3B33UROB if the student is concurrently registered for or has previously completed the course B3B33VIR (mutually exclusive courses).
Garant předmětu:
Karel Zimmermann
Lecturer:
Karel Zimmermann
Tutor:
David Čapek, Aleš Kučera, Tomáš Tichý, Patrik Vacek, Jan Vlk, Karel Zimmermann
Supervisor:
Department of Cybernetics
Synopsis:

The course teaches deep learning methods on known robotic problems, such as semantic segmenation or reactive motion control. The overall goal is timeless universal knowledge rather than listing all known deep learning architectures. Students are assumed to have working prior knowledge of mathematics (gradient, jacobian, hessian, gradient descend, taylor polynomial) and machine learning (bayes risk minimization, linear classifier). The labs are divided into two parts, in the first one, the students will solve elementary deep ML tasks from scratch (including the reimplementation of autograd backpropagation), in the second one, students will build on existing templates in order to solve complex tasks including RL, tranformers and generative networks.

Requirements:
Syllabus of lectures:

Machine learning 101: model, loss, learning, issues, regression, classification

Under the hood of a linear classifier: two-class and multi-class linear classifier on RGB images

Under the hood of auto-differentiation: Computational graph of fully connected NN, Vector-Jacobian-Product (VJP) vs chainrule and multiplication of Jacobians.

The story of the cat's brain surgery: cortex + convolutional layer and its Vector-Jacobian-Product (VJP)

Where the hell does the loss come from? MAP and ML estimate, KL divergence and losses.

Why is learning prone to fail? - Structural issues: layers + issues, batch-norm, drop-out

Why is learning prone to fail? - Optimization issues: optimization vs learning, KL divergence, SGD, momentum, convergence rate, Adagrad, RMSProp, AdamOptimizer, diminishing/exploding gradient, oscillation, double descent

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,

Reinforcement learning: Approximated Q-learning, DQN, DDPG, Derivation of the policy gradient (REINFORCE), A2C, TRPO, PPO, Reward shaping, Inverse RL, Applications,

Memory and attention: recurrent nets, Image transformers with attention module

Generative models: GANs and diffusion models

Implicit layers: Backpropagation through unconstrained and constrained optimization problems, ODE solvers, roots, fixed points) + existing end-to-end differentiable modules cvxpy, gradSLAM, gradMPC, gradODE, pytorch3d

Syllabus of tutorials:

The labs are divided into two parts, in the first one, the students will solve elementary deep ML tasks from scratch (including the reimplementation of autograd backpropagation), in the second one, students will build on existing templates in order to solve complex tasks including RL, tranformers and generative networks.

Study Objective:
Study materials:

Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep learning, MIT press, 2016 http://www.deeplearningbook.org

Note:
Further information:
https://cw.fel.cvut.cz/wiki/courses/b3b33urob/start
Time-table for winter semester 2023/2024:
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon
roomKN:E-301
Zimmermann K.
14:30–16:00
(lecture parallel1)
Karlovo nám.
Šrámkova posluchárna K9
roomKN:E-230
Vacek P.
Čapek D.

16:15–17:45
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
Tue
roomKN:E-230
Vacek P.
Kučera A.

09:15–10:45
(lecture parallel1
parallel nr.102)

Karlovo nám.
Laboratoř PC
Wed
roomKN:E-230

16:15–17:45
(lecture parallel1
parallel nr.103)

Karlovo nám.
Laboratoř PC
Thu
Fri
Time-table for summer semester 2023/2024:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-05-27
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet6651806.html