Natural Language Processing (Sommersemester 2023)
Lecturer:
Prof. Dr. Gerard de Melo
(Artificial Intelligence and Intelligent Systems)
Course Website:
https://moodle.hpi.de/course/view.php?id=416
General Information
- Weekly Hours: 4
- Credits: 6
- Graded:
yes
- Enrolment Deadline: 01.04.2023 - 07.05.2023
- Examination time §9 (4) BAMA-O: 17.08.2023
- Teaching Form: Lecture / Exercise
- Enrolment Type: Compulsory Elective Module
- Course Language: English
- Maximum number of participants: 40
Programs, Module Groups & Modules
- IT-Systems Engineering
- IT-Systems Engineering
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-K Konzepte und Methoden
- BPET: Business Process & Enterprise Technologies
- HPI-BPET-T Techniken und Werkzeuge
- DANA: Data Analytics
- HPI-DANA-K Konzepte und Methoden
- DANA: Data Analytics
- HPI-DANA-T Techniken und Werkzeuge
- DANA: Data Analytics
- HPI-DANA-S Spezialisierung
- CODS: Complex Data Systems
- HPI-CODS-K Konzepte und Methoden
- CODS: Complex Data Systems
- HPI-CODS-T Techniken und Werkzeuge
- CODS: Complex Data Systems
- HPI-CODS-S Spezialisierung
- SECA: Security Analytics
- HPI-SECA-K Konzepte und Methoden
- SECA: Security Analytics
- HPI-SECA-T Techniken und Werkzeuge
- SECA: Security Analytics
- HPI-SECA-S Spezialisierung
- Digital Health
- HPI-DH-DS Data Science for Digital Health
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-C Concepts and Methods
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-T Technologies and Tools
- DICR: Digitalization of Clinical and Research Processes
- HPI-DICR-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
- HPI-SSE-D Data Foundations
- DSYS: Data-Driven Systems
- HPI-DSYS-C Concepts and Methods
- DSYS: Data-Driven Systems
- HPI-DSYS-T Technologies and Tools
- DSYS: Data-Driven Systems
- HPI-DSYS-S Specialization
- MALA: Machine Learning and Analytics
- HPI-MALA-C Concepts and Methods
- MALA: Machine Learning and Analytics
- HPI-MALA-T Technologies and Tools
- MALA: Machine Learning and Analytics
- HPI-MALA-S Specialization
Description
This course covers a broad range of topics relating to natural language processing (NLP) and computational linguistics, ranging from traditional linguistically oriented tasks such as syntactic parsing to modern deep learning-based NLP using Transformers (e.g., BERT, GPT-3, ChatGPT). It will cover recent techniques such as for prompt optimization and data augmentation, as well as applications such as conversational agents, machine translation, and sentiment analysis.
Requirements
This course requires solid programming skills as well as high school-level mathematics (especially basic linear algebra and probability theory).
Some prior familiarity with machine learning is recommended. Prior familiarity with deep learning is helpful, but not required.
Learning
Lectures with some integrated tutorial sessions
Examination
The grade is based exclusively on a written exam.
As a precondition to being able to take the exam, students are required to complete a series of homework assignments to a sufficient degree (50% of points on each).
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