Introduction to Deep Learning (I2DL) (IN2346)

Welcome to the Introduction to Deep Learning course offered in SS20.

Lecture

Mondays (14:00-16:00) - HOERSAAL MI HS 1 (00.02.001)

Until further notice, all lectures will be held online

Lecturers: Prof. Dr. Laura Leal-Taixé and Prof. Dr. Matthias Niessner.

ECTS: 6

SWS: 4

Due to covid-19, all lectures will be recorded!

Tutorial

Thursdays (08:00-10:00) - Interims Hörsaal 1 (5620.01.101)

Until further notice, all lectures will be held online

Tutors: Patrick Dendorfer and Andreas Rössler

Schedule

You can find the official schedule and information about this semester here

Content

• Lecture 1: Introduction to the lecture, Deep Learning, Machine Learning.
• Lecture 2: Machine Learning Basics: Linear regression, Classification and Loss Functions.
• Lecture 3: Introduction to neural networks
• Lecture 4: Backpropagation
• Lecture 5: Optimization
• Lecture 6: Training Neural Networks Part I
• Lecture 7: Training Neural Networks Part II
• Lecture 8: Training Neural Networks Part III
• Lecture 9: Convolutional Neural Networks (CNN) I
• Lecture 10: CNN II: common architectures, VGG, ResNet, Inception
• Lecture 11: Recurrent networks (RNN), LSTM
• Lecture 12: Advanced Deep Learning
• Lecture 13: Guest lecture

Slides

You can now download the slides in PDF format:

• Slides

Prerequisites

• Strong mathematical background: linear algebra, calculus.
• Previous knowledge of Python is needed.
• Machine Learning background is recommended.
• Previous knowledge of PyTorch is recommended.

Moodle

We use Moodle for discussions and to distribute important information. Please check the News and Discussion boards regularly or subscribe to them. The slides and all material will also be posted on Moodle.

FINAL EXAM:

• Exam date: tba

Contact us

If you have any questions regarding the organization of the course, do not hesitate to contact us at: i2dl@dvl.in.tum.de

For questions on the syllabus, exercises or any other questions on the content of the lecture, we will use the Moodle discussion board.

Office hours

Due to covid-19, all office hours will be online

External (non-TUM) Students

We want to provide access to our lecture for as many students as possible. Therefore, we ask external students that are not TUM students and do not have access to TUMonline to register to Moodle and send us their student information via email. We will then add you to our Moodle course where you will find addtional information and all the course material.