Deep Learning (Sommersemester 2021)
Lecturer:
Prof. Dr. Christoph Lippert
(Digital Health - Machine Learning)
Tutors:
M.Sc. Aiham Taleb
Jana Fehr
General Information
- Weekly Hours: 4
- Credits: 6
- Graded:
yes
- Enrolment Deadline: 18.03.2021 - 09.04.2021
- Teaching Form: Lecture / Seminar
- Enrolment Type: Compulsory Elective Module
- Course Language: English
Programs, Module Groups & Modules
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-C Concepts and Methods
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-T Technologies and Tools
- SCAD: Scalable Computing and Algorithms for Digital Health
- HPI-SCAD-S Specialization
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-C Concepts and Methods
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-T Technologies and Tools
- APAD: Acquisition, Processing and Analysis of Health Data
- HPI-APAD-S Specialization
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-K Konzepte und Methoden
- DATA: Data Analytics
- HPI-DATA-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-S Spezialisierung
Description
Lecture starts on April 14th, 2021
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. If you are not an HPI-student, you can get a guest account by contacting dhc-lab(at)hpi.de.
Examination
- Processing regular Math exercise sheets (All but one need to be passed to be eligible for the midterm)
- 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
Course starts on April 14th and ends on July 22nd, 2021.
- Lecture Q&A: Wednesdays, 2021 17:00 -18:30pm on Zoom
- Tutorial: Thursdays 17:00 -18:30pm on Zoom
- Exam: June 17th, 2021
If you have questions or problems, please contact teaching-lippert(at)hpi.de
Zurück
