de | en

Introduction to Deep Learning (IN2346)

Course 0000002767 in SS 2018

General Data

Course Type lecture with integrated exercises
Semester Weekly Hours 4 SWS
Organisational Unit Informatics 15 - Chair of Computer Graphics and Visualization (Prof. Westermann)
Lecturers Laura Leal-Taixe
Quirin Lohr
Matthias Nießner
Sabine Wagner
Dates Mon, 14:00–16:00, MI HS1
Thu, 08:00–10:00, Interims I 101
and 1 singular or moved dates

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks - Introduction to Computer Vision and history of Deep Learning. - Machine learning Basics 1: linear classification, maximum likelihood - Machine learning basics 2: logistic regression, perceptron - Introduction to neural networks and their optimization, SGD, Back-propagation - Training Neural Networks Part 1: regularization, activation functions, weight initialization, gradient flow, batch normalization, hyperparameter optimization - Training Neural Networks Part 2: parameter updates, ensembles, dropout - Convolutional Neural Networks - CNN for object detection (from MNIST to ImageNet), visualizing CNN (DeepDream) - Recurrent networks and LSTMs - Research 1: Prominent architectures, e.g. GoogleNet, ResNet - Research 2: Reinforcement learning - Research 3: Adversarial networks
Links E-Learning course (e. g. Moodle)
TUMonline entry

Equivalent Courses (e. g. in other semesters)

SemesterTitleLecturersDates
WS 2022/3 Introduction to Deep Learning (IN2346) Chen, Y. Dahnert, M. Dai, A. Huang, J. singular or moved dates
SS 2022 Introduction to Deep Learning (IN2346) Dendorfer, P. Leal-Taixe, L.
Assistants: Gerken, F.
Mon, 14:00–16:00, MI HS1
Tue, 14:00–16:00, GALILEO Audimax
and singular or moved dates
WS 2021/2 Introduction to Deep Learning (IN2346) Dahnert, M. Franzmann, A. Gafni, G. Nie, Y. Nießner, M. Tue, 14:00–16:00, virtuell
Thu, 10:00–12:00, virtuell
SS 2021 Introduction to Deep Learning (IN2346) Franzmann, A. Nießner, M. Wagner, S. Weitz, S. Mon, 14:00–16:00, virtuell
Thu, 08:00–10:00, virtuell
and singular or moved dates
WS 2020/1 Introduction to Deep Learning (IN2346) Dai, A. Dendorfer, P. Leal-Taixe, L. Lohr, Q. Nießner, M. … (total 6) Tue, 14:00–16:00, virtuell
Thu, 10:00–12:00, virtuell
and singular or moved dates
SS 2020 Introduction to Deep Learning (IN2346) Dendorfer, P. Leal-Taixe, L. Lohr, Q. Nießner, M. Rössler, A. … (total 7)
WS 2019/20 Introduction to Deep Learning (IN2346) Dai, A. Leal-Taixe, L. Lohr, Q. Nießner, M. Rössler, A. … (total 6) Tue, 14:00–16:00, MI HS1
Thu, 10:00–12:00, MI HS1
and singular or moved dates
SS 2019 Introduction to Deep Learning (IN2346) Leal-Taixe, L. Lohr, Q. Nießner, M. Rössler, A. Wagner, S. Thu, 08:00–10:00, Interims I 101
Mon, 14:00–16:00, MI HS1
WS 2018/9 Introduction to Deep Learning (IN2346) Leal-Taixe, L. Lohr, Q. Nießner, M. Wagner, S. Thu, 18:00–20:00, MI HS1
Tue, 18:00–20:00, Interims I 101
and singular or moved dates
SS 2017 Deep Learning for Computer Vision (IN2346) Häfner, B.
Responsible/Coordination: Cremers, D.
Assistants: Frerix, T.Leal-Taixe, L.Nießner, M.
Top of page