Introduction to Deep Learning (Sommersemester 2022)

Lecturer: Prof. Dr. Christoph Lippert (Digital Health - Machine Learning) , Tahir Miriyev (Digital Health - Machine Learning) , Eshant English (Digital Health - Machine Learning) , Wei-Cheng Lai (Digital Health - Machine Learning) , Noel Danz (Digital Health - Machine Learning)

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 01.04.2022 - 30.04.2022
• Examination time §9 (4) BAMA-O: 06.07.2022
• Teaching Form: Lecture / Seminar
• Enrolment Type: Compulsory Elective Module
• Course Language: English

Programs, Module Groups & Modules

Description

Lectures & Tutorials start on April 27 and 28, respectively.

This course is designed to give students an in-depth introduction to deep learning and review the state of the art in important applications, such as computer vision and language modeling. The lectures and exercises are designed around fundamentals and use cases and will use real-world data to gain practical experience with machine learning models and algorithms. The course will start with an introduction to the basic concepts of machine learning and empirical data analysis, such as model fitting, selection and validation. During the second part of the course, we will discuss supervised machine learning, starting with linear models, to non-linear models, including deep neural networks, convolutional neural networks and sequence models.  During the third part of the course, we will discuss unsupervised learning, generative models and self-supervision.

Due to significant overlap in course material, this course cannot be combined with the Machine Learning for Precision Medicine from Summer Semester 2019 and neither with the Introduction to Deep Learning Course from Summer Semester 2020.

Learning Objectives:

• Understand concepts, methods and algorithms in machine learning
• Ability to empirically analyze real-world data
• Ability to assess the quality and validity of a machine learning model for a given analysis
• Ability to select, develop, implement and apply appropriate machine learning models and algorithms for a given use case.

Requirements

Maths prerequisites: Linear Algebra (vectors, matrices), Calculus (derivatives), Probabilities (we will refresh them in the first tutorial)

Programming prerequisites: Beginner level in python, syntax (functions, loops, etc.) and its libraries numpy and pandas.

Literature

This course will be based on the material provided at http://d2l.ai/

Learning

Online tools include

• Zoom for Meetings
• HPI Moodle course: https://moodle.hpi.de/course/view.php?id=278
  • If you are not an HPI-student, you can get a Moodle guest account by contacting dhc-lab(at)hpi.de.
  • You do not need an HPI guest account or anything else HPI-specific!
  • For further questions, please consult the teaching team directly.

Examination

• Processing regular Math exercise sheets
• Programming project assignment in teams of 4-5 people during the second half of the course: Code + Final report (40% of final grade)
• Final exam (60% of final grade)

Dates

First lecture: 27th of April
First Tutorial Session: 28th of April

• Lecture Q&A:  Wednesdays, 17:00 -18:30pm
• Tutorial: Thursdays 17:00 -18:30pm

HPI Moodle https://moodle.hpi.de/course/view.php?id=278

If you have questions or problems, please contact teaching-lippert(at)hpi.de

written exam: 6 July 2022 room: HS 2 , 15:15pm

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