Search Engines (SE)
Course content
The course objective is to offer an advanced introduction into search engines. The goal is to understand and model how search engines collect information, transform it and store it internally, and then operate on it in order to satisfy user queries. Through realistic and sound projects, the course aims to stimulate and prepare students for their MSc thesis work.
The course will focus on these main questions:
- How can we design efficient retrieval systems?
- How can we design effective retrieval systems?
Content in detail:
Architecture of an IR system
- Basic building blocks
- Crawling, filtering and storing information
- Ranking with indexes
Information ranking models
- Probabilistic & machine learning models
- Complex queries and combining evidence
- Deep learning for search engines
- Evaluation and optimisation
MSc Programme in Computer Science
Knowledge of
- The basic architecture of search engines
- The basic models and techniques for collecting, storing and ranking information
- Different criteria for search engine evaluation
Skills in
Students should be able to transfer the above knowledge to real-world tasks by:
- Designing appropriate strategies for crawling, storing and ranking information
- Planning and carrying out appropriate evaluations
Given a working search engine, students should be able to:
- Diagnose problems in its main information processing functions
- Design and calibrate appropriate solutions
Competences to
- Explain the basic search engine principles to both laymen and specialists
- Use standard procedures and practices when designing or implementing search engine solutions
- Present evaluation analyses and results in a proper format of a written report such that a technically qualified person can follow and obtain similar findings
The course will use a combination of lectures (2 hours per week)
and lab sessions (2 hours per week). Lectures and labs might
include discussions, group activities, and student presentations.
Where possible, relevant guest lecturers will be involved.
Students will carry-out a project which consists of both practical
exercises (implementing state of the art solutions) and theoretical
questions (to reflect on the course content in relation to the
project). The project will cover the main topics presented during
the lectures.
The literature consists of seminal research and review articles
from central journals and selected papers from peer-reviewed
conferences, textbooks and research reports. This is supplemented
with practical experience gained through lab sessions.
The literature will be listed in Absalon.
It is expected that students know how to program and have a
working knowledge of Machine Learning corresponding to the course
Machine Learning (ML) or an equivalent course.
Academic qualifications equivalent to a BSc degree is
recommended.
The course is identical to NDAK20002U Neural Information Retrieval
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral exam on basis of previous submission, 20 minutes (no preparation)
- Type of assessment details
- Specifically, the exam consists of two parts:
1. An individual report (written assignment) based on the project.
2. An individual oral examination (without preparation) based on the report.
The written and oral examination are not weighted. Only one overall assessment is provided for the two parts of the exam. - Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
-
Same as the ordinary exam.
For the re-exam the student must complete a new project and submit a new report. The deadline for submitting the new report will be published in Absalon.
Additionally the 20-minutes oral examination without preparation will be administered covering the full course syllabus.
The written and oral examination are not weighted. Only one overall assessment is provided for the two parts of the exam.
It is not possible to reuse parts of the exam at a later exam.
Criteria for exam assessment
See Learning Outcome
Single subject courses (day)
- Category
- Hours
- Lectures
- 32
- Preparation
- 80
- Project work
- 71
- Exam Preparation
- 22
- Exam
- 1
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NDAK24004U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 4
- Schedulegroup
-
B
- Capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
- Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Computer Science
Contracting faculty
- Faculty of Science
Course Coordinator
- Christina Lioma (7-65306e6b716f6342666b306d7730666d)
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Kursusinformation for indskrevne studerende