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Kursusnavn (dansk):Second Year Project 
Kursusnavn (engelsk):Second Year Project 
Semester:Forår 2019 
Udbydes under:Bachelor i datavidenskab (b-ds) 
Omfang i ECTS:15,00 
Min. antal deltagere:
Forventet antal deltagere:
Maks. antal deltagere:50 
Formelle forudsætninger:The course description will be available beginning November 2018 
Læringsmål:- Discuss, clearly explain, and reflect upon central concepts, algorithms, and challenges in natural language processing (NLP) and deep learning (DL).
- Organize, plan, and carry out collaborative work in a smaller project group.
- Obtain, scrub, explore and preprocess a wide range of relevant raw data for a given problem. - Identify and analyze the relevant options for data collection and preprocessing and select the most suitable ones.
- Design and implement a sound experiment in NLP
- Distinguish and evaluate the advantages of different design choices or approaches to the same task (e.g., traditional versus deep-learning based solutions)
- Evaluate the achieved solution and carry out a detailed error analysis, relating the findings back to the overall problem domain
- Explain in writing (project group report) adhering to academic standards in writing
- Succinctly present the results of the project, discuss findings and limitations
- Reflect upon ethical considerations that arise in the deployment of language technology 
Fagligt indhold:This course covers:
An introduction to major topics in Natural Language Processing and relevant related topics (traditional and neural-network based methods to, e.g., language modeling, classification, sequence processing).
An introduction to deep neural networks for Natural Language Processing, including representation learning, and an implementation in a corresponding Python-based framework.

In this project-based course, students are going to work on a real-world problem using natural language processing technology. The focus of the theoretical part of this class is on natural language processing and deep learning. Social implications of data handling will be discussed as well.

Amongst others, the following topics will be covered

- Introduction to NLP and DL, what makes language so difficult; traditional versus neural approaches
- Data Science at the command line; obtaining and preprocessing data
- Language Models, Neural LMs
- Classification for NLP, Embeddings
- Sequence predictions for NLP, RNNs and LSTMs
- Ethical considerations of machine learning 
Læringsaktiviteter:14 ugers undervisning bestående af forelæsninger, øvelser og vejledning

The course has two parts: a series of lectures and exercises with a possibility for guest lecturers followed by project-based work in groups. 

Obligatoriske aktivititer:Der er ingen obligatoriske aktiviteter. Vær venlig KUN at ændre denne tekst når der er obligatoriske aktiviteter./
There are no mandatory activities. Please, change this text ONLY when there are mandatory activities. 
Eksamensform og -beskrivelse:D2G Aflevering med mundtlig eksamen der supplerer projekt. Delt ansvar for projekt., (7-scale, external exam)

Duration of oral examination 20 minutes per student

Mixed exam 1:
The students make a joint presentation followed by a group dialogue. Subsequently the students are having individual examination with dialogue with the supervisor and external examiner while the rest of the group is outside the room.

Group size 4-5 students
Hand in 2 files:
- Project report as PDF file
- Source code as .zip file  

Følgende personer underviser på kurset:
NavnStillingUndervisertypeIndsats (%)
Barbara Plank Lektor(ITU) Kursusansvarlig 100
Marija Stepanovic Undervisningsassistent(ITU) Hjælpelærer 0
Sarah McGillion Undervisningsassistent(ITU) Hjælpelærer 0