Open Course Materials

INF1-CG: Informatics 1 - Cognitive Science

mcorey — Mon, 15 Jan 2024 13:44:43 +0000

INF1-CG: Informatics 1 - Cognitive Science mcorey Mon, 15/01/2024 - 13:44

Please find all materials for this course here.

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SDP: System Design Project

aburford — Wed, 20 Dec 2023 09:47:58 +0000

SDP: System Design Project aburford Wed, 20/12/2023 - 09:47

2023-2024: We will not be posting course information here this year. Please refer to the course's Learn page for latest information and updates.

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MIP2: MInf Project (Part 2)

mcorey — Tue, 28 Nov 2023 12:03:04 +0000

MIP2: MInf Project (Part 2) mcorey Tue, 28/11/2023 - 12:03

Please find all materials for this course here.

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MIP1: MInf Project (Part 1)

mcorey — Tue, 28 Nov 2023 11:59:20 +0000

MIP1: MInf Project (Part 1) mcorey Tue, 28/11/2023 - 11:59

Please find all materials for this course here.

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PROJ: Honours Project (Informatics)

mcorey — Tue, 28 Nov 2023 11:18:01 +0000

PROJ: Honours Project (Informatics) mcorey Tue, 28/11/2023 - 11:18

Please find all materials for this course here.

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PMR: Probabilistic Modelling and Reasoning

aburford — Thu, 23 Nov 2023 15:59:03 +0000

PMR: Probabilistic Modelling and Reasoning aburford Thu, 23/11/2023 - 15:59

Lecturer (wks 1-5): Chris Williams
Lecturer (wks 6-11): Michael Gutmann
Teaching assistant: Vaidotas Simkus
Lecture/tutorial times and locations: please see here.

Content

The course provides you with a thorough understanding of statistical/probabilistic machine learning methods that are used in a wide range of different fields with a focus on unsupervised learning. The course covers four main topics:

probabilistic graphical models,
exact inference,
learning,
approximate inference and learning.

The course material is available here and optional material here.

Assessment

Overview

The course is assessed by a written exam at the end of the course (75% of final mark) and three quizzes during the semester (25% of final mark). There is no assignment.
The quizzes check your engagement with the course and are typically easier than the exam.
The exam date is announced here and past exams are available here (search for probabilistic modelling and reasoning).
PMR follows the Common Marking Scheme of the university and the school-wide academic conduct policy.

Quizzes

The quizzes open 1 week prior to their due date. They are time-limited and you will you have 1 attempt for each quiz.
The maximal time to complete a quiz is 60 minutes. The 60 mins will allow you to consult the slides or tutorials, but you will still need to study the material beforehand. Otherwise, you will likely run out of time.
The quizzes are available on gradescope. Please access it via Learn.
The quizzes will be autosaved and you can revise them as often as you want but once you have started a quiz, you will have to finish it within 60 minutes after you started.
When a quiz “closes at 4pm”, it means that you can work on it until 4pm.

✔ Test quiz: Opens Monday Feb 05 at 9am, closes Friday Feb 09 at 4pm. This is to check that you can access gradescope and allows you to familiarise yourself with the types of questions asked in the quizzes. It takes less than five minutes to complete. Please get in touch with the TA in case of access problems or questions.

✔ Quiz 1: Opens Friday Feb 09 at 9am, closes Friday Feb 16 at 4pm (end of week 5). The quiz is on directed and undirected graphical models. It covers the material on the slides until (and including) “Undirected Graphical Models II”, and the exercises discussed in tutorials 1 and 2.

✔ Quiz 2: Opens Friday March 1 at 9am, closes Monday March 11 at 4pm (start of week 8). The quiz is on inference and message passing. It covers the material on the slides “Factor Graphs” and “Exact Inference”, and the exercises discussed in tutorial 4.

Quiz 3: Opens Friday March 15 at 9am, closes Monday March 25 at 4pm (start of week 10). The quiz is on learning. It covers the slides “Basics of Model-Based Learning”, “Factor Analysis and Independent Component Analysis”, and Tutorial 6. Note: you will have to do some calculations by hand. So please have pen and paper ready.

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Creative Commons - Attribution

Data Literacy for Teachers

aburford — Thu, 23 Nov 2023 11:15:12 +0000

Data Literacy for Teachers aburford Thu, 23/11/2023 - 11:15

About the course

Welcome to Data Literacy for Teachers! In this course, you will learn about practical techniques for teaching data literacy in broad general education (5-14 years old). The course takes place over 5 months. Each month will cover a different topic which you will explore by:

Reading articles and watching videos (2 hours)
Attending an online class (2 hours)
Trying out a data-related activity and preparing to teach it to your class (2 hours)
Reflecting and writing feedback on the activity (2 hours)
Sharing what you learned with colleagues (1 hour)
Here is a list of general resources which you will find helpful throughout the course.

Topics

Topic 1: Introduction and problem solving with data

Topic 2: Data visualisations

Topic 3: Data for fictional worlds

Topic 4: Community data

Topic 5: Sensing our world

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DISS: MSc Dissertation (Informatics)

flittlet — Tue, 31 Oct 2023 15:49:10 +0000

DISS: MSc Dissertation (Informatics) flittlet Tue, 31/10/2023 - 15:49

The project is an essential component of the Masters courses. It is a substantial piece of full-time independent work starting in June. A dissertation describing the work must be submitted by a deadline in mid-August.

Students are expected to stay in Edinburgh for the duration of their degree programme. This includes during the writing of the MSc dissertation until the submission deadline. If you are on a Tier 4 visa and leave the country for an extended period of time, the School is obligated to contact Student Immigration Service who will notify UK Visa and Immigration (UKVI).) See MSc handbook.

Timetable

Date	Deadline / event
4/12/2023	DPMT system opens for MSc project proposals.
19/1/2024	Deadline for all project proposals (including self-proposed projects).
26/1/2024	Students start to register interest in projects and meet with potential supervisors. Supervisors mark interested students as suitable/unsuitable for projects.
2/2/2024	Students should have registered interest in projects, to provide time to meet with prospective supervisors. Supervisors are encouraged to review interested students for suitability at this stage.
9/2/2024	Project selection phase ends. It is no longer possible for students to register for new projects. Supervisors have marked all interested students as either suitable or unsuitable. Each student has ranked interesting projects in order of preference and is marked suitable for at least 5 projects.
12/2/2024	Project allocation begins. To be completed during the rest of the week.
23/2/2024	Project allocation announced to students.
1/3/2024	Special cases processed. Deadline for changes to projects and supervisors.
End of April 2024	Final IPP Submission; see IPP page.
beginning of June 2024	Students start work on MSc projects, based on their IPP.
July 2024 (TBA)	Stage 2 BoE officially makes progression decisions.
12/7/2024	Submission of project progress reports.
23/8/2024	Submission of dissertation.
September 2024	First and second markers complete their project marking.
September	First and second markers agree a mark for the project, or fail to agree a mark, and the supervisor fills in the agreed (or failure to agree) mark form.
September/October	All project moderation complete.
September/October	Stage 2 BoE award decisions
October	Final MSc BoE

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INF2-SEPP: Informatics 2 - Software Engineering and Professional Practice

flittlet — Tue, 31 Oct 2023 15:12:22 +0000

INF2-SEPP: Informatics 2 - Software Engineering and Professional Practice flittlet Tue, 31/10/2023 - 15:12

Welcome to Informatics 2 - Software Engineering and Professional Practice

Learning Outcomes

On successful completion of this course, you should be able to:

Explain the modern techniques used in the design and development of large-scale software systems
Apply, evaluate and reflect on these techniques in a small-scale, but realistic scenario
Analyse the professional and ethical implications of software engineering decisions and propose solutions
Comfortably read and write technical documentation.
Constructively engage in interaction with peers.

Course Outline

As students enter this course they team up in groups of 3-4 to develop a small-scale software system from scratch. Over the course of the semester, they consider an incomplete specification to derive and analyse requirements, design their solution from a static and dynamic perspective using UML diagrams, construct and test their solution in Java. There is room for interpretation, creativity, and some of the requirements change along the way. Moreover, there are technical, professional and ethical issues surrounding the problem at hand, on which students will need to reflect.

Included in the experience will be use of industry standard tools for software development (integrated development environments, version control, issue tracking), and key elements of modern development practice, such as code review, peer review, and pair programming.

As students engage in this practical work, the course will contextualise it against the broader themes, both of large-scale software engineering and its academic literature, and of today's urgent professional issues: the legal, ethical and social context in which software and its authors exist. Guest lecturers will speak on topics with direct impact on students' practical work, but also advising on becoming a successful software engineer.

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MLG: Machine Learning

flittlet — Mon, 23 Oct 2023 15:58:28 +0000

MLG: Machine Learning flittlet Mon, 23/10/2023 - 16:58

The course website is at https://homepages.inf.ed.ac.uk/htang2/mlg2023/

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