COMP SCI 4817 - Applied Natural Language Processing Honours
North Terrace Campus - Semester 1 - 2025
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General Course Information
Course Details
Course Code COMP SCI 4817 Course Applied Natural Language Processing Honours Coordinating Unit Computer Science Term Semester 1 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange N Assumed Knowledge Calculus, Linear Algebra, Probability and Statistics, Python programming, Basics of Machine Learning Assessment Quiz, exam Course Staff
Course Coordinator: Dr Alfred Krzywicki
Course Timetable
The full timetable of all activities for this course can be accessed from .
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Learning Outcomes
Course Learning Outcomes
No information currently available.
University Graduate Attributes
No information currently available.
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Learning & Teaching Activities
Learning & Teaching Modes
No information currently available.
Workload
No information currently available.
Learning Activities Summary
No information currently available.
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Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Two assignments (15% and 30% of total grade) involving design and coding practice for solving NLP problems.
10 weekly mini-quizes (10% of the grade in total) focused on the theoretical part of the course. Each mini-quiz will take one hour or less to complete.
Final test (40% of the grade) focused on the theoretical part of the course.
Some mark (up to 5%) for active participation may also be awarded.Assessment Related Requirements
Python coding practice, knowledge of course material and basic Machine Learning skills are required to complete assessments.Assessment Detail
Weekly quizzes in weeks 1-10
Due: each week 1-10
Percentage of grade: 10%
Type: Quiz, individual
Assignment 1: Text classification and a sentiment analysis system
Due: Week 4
Percentage of grade: 15%
Type: Open-book, individual
Assessment Documents: Code and report
Assignment 2: NLP application project: building an information retrieval, question answering and a dialog system
Due: Week 11 (tentative)
Percentage of grade: 30%
Type: Open-book, individual or group
Assessment Documents: Code and Report
Final Test: NLP Algorithms and Applications
Due: Week 13 (tentative)
Percentage of grade: 40%
Type: Open-book quiz, individual
Remaining 5% of grade will be awarded for active participation in workshops.Submission
Submission details for all activities are available in MyUni but the majority of your submissions will be online and may be subjected to originality testing through Turnitin or other mechanisms. You will receive clear and timely notice of all submission details in advance of the submission date.Course Grading
Grades for your performance in this course will be awarded in accordance with the following scheme:
M11 (Honours Mark Scheme) Grade Grade reflects following criteria for allocation of grade Reported on Official Transcript Fail A mark between 1-49 F Third Class A mark between 50-59 3 Second Class Div B A mark between 60-69 2B Second Class Div A A mark between 70-79 2A First Class A mark between 80-100 1 Result Pending An interim result RP Continuing Continuing CN Further details of the grades/results can be obtained from Examinations.
Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.
Final results for this course will be made available through .
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Student Feedback
The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.
SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy (http://www.adelaide.edu.au/policies/101/) course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.
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Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
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Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
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Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
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