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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Language Sciences

Postgraduate Course: Computer Programming for Speech and Language Processing (LASC11096)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course introduces the concept of computer programming and the python programming language. It focuses on how to think about solving problems in ways that can be addressed algorithmically, with with examples relevant to processing human speech and language.
Course description This course covers high-level concepts in computer programming as well as hands-on practical training in writing code in python. The goal is to teach students to think about computational approaches to solving problems - what is a computer? what can it do? how can a question be framed in computational terms? In addition, students learn the nuts and bolts of programming in python including data structures, flow of control, input and output. The details are taught within the context of problems in speech and language processing.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2020/21, Available to all students (SV1) Quota:  120
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 27, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 71 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework
Feedback The formative feedback will comprise:
1. Tutor/lecturer interaction and feedback. The course operates along the lines of a flipped classroom model: students engage with the course website in their own time to cover all pre-recorded video and text material, and then spend up to 4 hours per week working on programming problems and other exercises under supervised lab conditions, i.e. with the lecturer and/or other experienced python programmers on hand to give advice and answer questions. This greatly increases opportunities for targeted interaction with the lecturer, tutors and fellow classmates.

2. Some exercises employ automated testing within a standard software engineering unit-testing framework, invoked at will by the student, which will give instant feedback on how successful each programming attempt has been. These are not part of summative assessment, but the lecturer can monitor pass/fail rates per student and exercise over time, to target students or topics that need more attention.

3. Short multiple choice quizzes will be used at points throughout the course, both at the whole-class lectures and the in-lab learning setting. Experience has shown students enjoy and appreciate these, as they provide feedback to students about their own level of understanding and developing knowledge.

4. Code review exercises are included at various points, including discussion of carefully chosen examples of good and bad computer code and double-blind peer-review of students' own code. Such code review exercises assist learning by giving opportunities to consider varying code styles and standards, critique the code of others, and to receive informal feedback on one┐s own coding performance. Code review is widely used in industry, so this is also a valuable transferable skill.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. understand and employ the basic principles of computer programming
  2. apply familiarity with the basics of the Python language to write useful programmes
  3. analyse and address problems from a computational perspective
  4. create programmes to manipulate, reorganise and process data (in particular speech and text) in non-trivial ways
  5. review and critique computer code written by others
Reading List
┐Learning Python┐ by Mark Lutz is the recommended reading.
In addition, pointers to other online resources for further reader are included on the course website.
Additional Information
Graduate Attributes and Skills Python coding and code review skills, with particular emphasis on speech and natural language processing.
KeywordsSpeech,Computer programming,Python
Course organiserDr Korin Richmond
Tel: (0131 6)51 1769
Course secretaryMiss Toni Noble
Tel: (0131 6)51 3188
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