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Overview

Course Prescription

Explores essential concepts and technologies in state-of-the-art deep neural network architectures, including convolutional neural networks, decision trees, random forests, similarity learning, recurrent neural networks, and long short-term memory networks. Includes hands-on experience combining hardware components with software implementations.

Course Overview

The class "Machine Intelligence and Deep Learning" (COMPSYS721) overviews essential concepts and technologies in deep neural network architectures. During the 12-week course, students will learn to implement and train their neural networks and gain a detailed understanding of cutting-edge research in machine learning and deep neural networks. Furthermore, through the labs and the course projects, students will develop the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks. The final project will be on training the Jetson nano module to do object tracking and face recognition tasks. The course will mainly discuss the following topics:  Convolutional Neural Networks (CNNs): Students delve into CNN principles, advanced methods, and hands-on practice to equip them for applying CNNs in real-world situations. The curriculum covers both theoretical and practical implementation using Python and popular frameworks.  Decision Trees and Random Forests: Students learn how these algorithms organize data for predictions, with Random Forests expanding on Decision Trees to handle more intricate data sets while enhancing accuracy. Similarity Learning with Siamese Neural Networks: This section introduces how to gauge object similarity using Siamese Networks utilizing machine learning techniques. It explores applications like face recognition, examines loss functions like Triplet loss, and discusses One Shot and Few Shot Learning.  Recurrent Neural Networks (RNNs) and LSTM: The course explores RNNs tailored for time series data, introducing LSTM networks to tackle long-term dependencies for improved performance in tasks like language translation and time series forecasting. Throughout the course, participants engage in lab exercises and real-world scenarios to practice their skills in applying these algorithms to machine learning challenges. There will be projects using hardware components and software implementations.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures, a 1 hour tutorial, 3 hours of reading and thinking about the content and 5 hours of work on assignments and/or test preparation.

Course Prerequisites, Corequisites and Restrictions

Prerequisite

Advice on Course Limits

This is a limited entry course: there is a limit on the number of enrolments due to staff or space capacity. In cases where the courses is taught under two separate codes (e.g. concurrently taught courses, general education courses) the course limit specified is the total across both versions of the course. For more information, please see the Programme and Course Limitations section of the University Academic and General Statutes and Regulations.

Locations and Semesters Offered

LocationSemester
City

Teaching and Learning

Campus Experience

Attendance is must/expected at scheduled activities including must for the labs/ expected for the lectures to receive credit for components of the course.
Lectures will be available as recordings. Other learning activities including labs/studios will not be available as recordings.
The course will not include live online events including group discussions/tutorials.
Attendance on campus is required for the test.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Taught courses use a learning and collaboration tool called Canvas to provide students with learning materials including reading lists and lecture recordings (where available). Please remember that the recording of any class on a personal device requires the permission of the instructor.

Copyright

The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.


You may copy the course content for the purposes of private study or research, but you may not upload onto any third-party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Health and Safety

Health and safety conditions when using MD'S and/or ESE research labs require a certificate of passing induction training. Students must ensure they are familiar with their Health and Safety responsibilities, as described in the university's Health and Safety policy

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Similarly, research students must meet the University’s expectations of good research practice. This requires:

  • Honesty - in all aspects of research work
  • Accountability - in the conduct of research
  • Professional courtesy and fairness – in working with others
  • Good stewardship – on behalf of others
  • Transparency – of research process and presentation of results
  • Clarity - communication to be understandable, explainable and accessible

For more information on the University’s expectations of academic integrity, please see the Academic Conduct section of the University policy hub.

Disclaimer

Elements of this outline may be subject to change. The latest information about taught courses is made available to enrolled students in Canvas.

Students may be asked to submit assessments digitally. The University reserves the right to conduct scheduled tests and examinations online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.


Assessment and Learning Outcomes

Course Learning Outcomes

CLO #OutcomeProgramme Capability Link
1
2
3
4
5
6

Assessments

Assessment TypeAssessment PercentageAssessment Classification

Additional Information on Assessment

A passing mark is 50% or higher for each assessment category, according to the University policy.

All assessments are compulsory for all students and DNC for the course will be awarded if the student has not completed labs and not submitted the deliverable for any component (assignment or project) as required. The details of each assessment and requirements will be given via the course page on Canvas.

By default, late submissions are not allowed, unless specific late submission penalties are released on Canvas.  

Assessment to CLO Mapping

Assessment Type123456

Student Feedback, Support and Charter

Student Feedback

Feedback on taught courses is gathered from students at the end of each semester through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions. Your feedback helps teachers to improve the course and its delivery for future students. In addition, class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Additional Information on Student Feedback

The course will run for the second time in 2025. Based on the 2024 and 2025 SET results, students' feedback was excellent, and we continue to follow the same methods with minor amendments on some components. 

Class representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Tuākana

Tuākana is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at Tuakana Learning Communities.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website.

Wellbeing

We all go through tough times during the semester, or see our friends struggling. There is lots of help out there - please see the Support Services page for information on support services in the University and the wider community.

Special Circumstances

If your ability to complete assessed work is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due. If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page. This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter.

Student Academic Complaints and Disputes

Students with concerns about teaching including how a course is delivered, the resources provided, or supervision arrangements, have the right to express their concerns and seek resolution. The university encourages informal resolution where possible, as this is quicker and less stressful. For information on the informal and formal complaints processes, please refer to the Student Academic Complaints Statute in the Student Policies and Guidelines section of the Policy Hub.