Attendance is required at scheduled activities including lectures and tutorials to complete components of the course.
Lectures and tutorials will be available as recordings, but attendance is crucial for you to complete the course. 
Attendance on campus is required for the test.
The activities for the course are scheduled as a standard weekly timetable.

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.

You are strongly recommended to bring a personal laptop in tutorials. If you do not own one, laptop loan options are available at the University of Auckland Student IT Hub:https://www.auckland.ac.nz/en/students/student-support/student-it-hub/student-long-term-laptop-loan.html
We do not have a mandatory textbook for the course, but we use a recommended textbook:

• Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition. O'Reilly.

Python coding requirements In this course, we will be using Python as programming language to implement and experiment with AI systems. We assume that students already have some experience with programming, especially using Python. You should be familiar with Python’s main scientific libraries, in particular Numpy., Pandas., and Matplotlib. If you're new to Python or need a refresher before diving into this course, we highly recommend completing the LearnPython tutorial (at least the "Learn the Basics" and "Data Science Tutorials"). Other options are the official tutorial at Python.org or the Learn Python Kaggle tutorial. The advantage of the Kaggle tutorial is that it is using notebooks, which we will use to edit and run code in the tutorials.

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.

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.

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.

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.

A pass mark in both the practical (assignments and project) and theory (test and exam) components of the course is required to pass the course.

The evaluation of the group project hackathon is done based on a group report and a group presentation.

Students are expected to attend all in-person activities, including lectures and tutorials. There will be a hackathon event organised in week 10 or 11, as part of the group project. The event will take place on Campus and is likely to be held over a week-end. 

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.

Based on student feedback in S1 2024, we plan to implement the following changes in S1 2025:

• We will organise a start of the semester workshop (before first week), which might cover: intro to Python, setting-up programming environment (including Docker), intro to ML/AI libraries (Keras/TensorFlow/Pytorch, …), setting-up CUDA for GPU. However, students should ideally already be familiar with programming in Python before joining the course.
• The 3h of lectures per week will be organise as a 1h session, and a 2h session, combining lecture and tutorial. This will hopefully make the in-person tutorial time more useful and provide direct application of the concepts discussed in the lecture.
• We will reduce the number of tutorial assignments from 5 to 4, to reduce the assignment load. The corresponding 10% of the marks are transferred to the group project, which will run as an hackathon. 

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 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.

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.

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.

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.

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.

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.