COMP SCI 4817 - Applied Natural Language Processing Honours

North Terrace Campus - Semester 1 - 2024

This course examines machine learning techniques that obtain leading results on the problem of natural language processing (NLP). NLP is a critical step towards effective communication between people and machines. You will learn how to represent words and text, the use of deep recurrent models for text prediction, and issues that separate NLP from other application domains. This will be reinforced by applying deep learning tools to NLP through examples and practical projects.

  • 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 COMP SCI 7317
    Course Description This course examines machine learning techniques that obtain leading results on the problem of natural language processing (NLP). NLP is a critical step towards effective communication between people and machines. You will learn how to represent words and text, the use of deep recurrent models for text prediction, and issues that separate NLP from other application domains. This will be reinforced by applying deep learning tools to NLP through examples and practical projects.
    Course Staff

    Course Coordinator: Dr Alfred Krzywicki

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes

    No information currently available.

    University Graduate Attributes

    No information currently available.

  • Learning & Teaching Activities
    Learning & Teaching Modes

    No information currently available.

    Workload

    No information currently available.

    Learning Activities Summary

    No information currently available.

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. 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)
    GradeGrade reflects following criteria for allocation of gradeReported 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 Access Adelaide.

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