Neural Networks and Deep Learning COMP9444 24T3

Tutorials time and assigned tutors

Posted by Sonit Singh Friday 06 September 2024, 03:45:23 PM.

Dear Students,

I can see lot of posts from students regarding team formation for the group project. I think there is a confusion, so thought of posting an announcement rather than answering them individually.

Ideally we want students to form a team within the same tutorial. This will help you to meet new people, learn from their strengths and complement theirs, foster collaboration, and improve coordination, all of which are important aspects of a group project. While at the beginning, you might find that this is not going to work, research shows that these are important skills as you will work with new people at your workplace when you graduate. In workplace, you will work with people from diverse backgrounds, have different working habits, and different ambitions, but research shows that working with diverse people promote better creativity, problem-solving, and decision-making.

However, we will be accommodating minimal changes where students want to form team across different tutorial groups. For that to happen, particular members should be happy to move to other members' tutorial time so that then they are in the same tutorial group. Basically, we will do some internal swaps without changing too much of the timetable. So, don't worry about this at this moment and enjoy your lectures and tutorials. I will provide more details when we actually start the project work.

Below is the list of all tutorials/mentoring sessions along with the assigned tutors.

Note: Central teams are adding more students so there might be an additional two tutorials, so this table is accurate as of today.

You can check your tutorials timetable on Moodle and also on WebCMS. This is just additional information about which tutor is taking your tutorial.

Kind regards,
Sonit

Course Activities

Posted by Sonit Singh Friday 06 September 2024, 11:02:37 AM.

Dear Students,

We have the following activities throughout the term:

1.Lectures

Lecture A: Tuesday (9am - 11am); Mathews Theatre A (K-D23-Theatre A)

Lecture B: Thursday (11am-1pm); Mathews Theatre A (K-D23-Theatre A)

2. Tutorials/Project Mentoring Sessions

Check your timetable for your respective sessions. There are exercises which you must attempt and show to your tutors/mentors in your respective tutorials/project mentoring sessions to get 1 mark which accounts for your class participation mark. Tutorials questions will be released on every Friday for the upcoming week’s tutorial. Following table shows list of various exercises for all the weeks and marks distribution.

We make these changes based on students’ feedback from the past few terms as many students struggle to work consistently throughout the term on the group project. These checkpoints are designed to make sure you are keeping up with weekly tasks and consistently taking actions to learn the technical material, and the skills required for the successful completion of the assignment and the group project. Each checkpoint is worth 1\% of your final grade. You will need to show your work to your tutor during the tutorial or mentoring session to get the weekly checkpoint mark. If you are not able to make up for a particular tutorial for any valid reasons, you can always show your work to your tutor/mentor in the next tutorial.

3. Assessments

Assignment : The assignment will be released in Week 3 and due towards the end of Week 5.

Group Project: It is suggested to start group project early. If you see the above table, you can notice that we have bold text which are skills and checkpoints for successful completion of the group project. Students should form a team (4 or 5 members) within the same tutorial. The tutors/mentors will be guiding you throughout the term for the successful completion of the group project.

Final Exam: The final exam will be in-person and invigilated at UNSW Kensington campus (more details on this later).

4. Additional Resources

Throughout the term, we will be sharing additional resources ( WebCMS3 > Additional Resources ), which can help you to have deeper understanding of the taught concepts and help you to know what current and state-of-the-art deep learning algorithms. It is recommended that you should read these additional resources.

We will be going through all these details in the first lecture. This term we have more than 850 students. While we strive to provide you all a memorable and enjoyable learning experience, please bear with us as managing large classes is a big challenge.

Kind regards,

Sonit

COMP9444 Learning Platforms

Posted by Sonit Singh Thursday 05 September 2024, 08:15:13 AM.

Dear Students,

As we navigate through the excitement of O-Week and look forward to the first lecture, please make sure that you have access to course materials across various learning platforms. We will be making use of mainly three learning platforms:

• Moodle: If you are officially enrolled in the course, you will get default access to the Moodle course page. Please Login to Moodle and make sure you can access course page "COMP9444-Neural Networks, Deep Learning - 2024 T3". We will be using Moodle only for Echo360 (lecture recordings) and for filling myExperience survey towards the end of the term.
• WebCMS3: Again, if you are officially enrolled in the course, you will get access to the WebCMS3 course page. On WebCMS, you can check Course Outline (see left menu items) and timetable (for lectures and tutorials). We will be releasing lecture slides (under Lectures) before the actual lectures (except first lecture)
  https://webcms3.cse.unsw.edu.au/COMP9444/24T3/

• Ed: Click on the link "Ed Link for the Course" and login to Ed. Familiarise yourself of Ed (mainly Discussions and Lessons), see icons on the top right of this Ed page. Please note that access to Ed platform is not automated and we need to manually add you to the course Ed page.

If you have any issues accessing Ed platform, please send an email to cs9444@cse.unsw.edu.au and we will follow it up.

I hope all of you will ensure that you have access to these learning platforms before our first lecture.

Kind regards,

Sonit Singh

Welcome to COMP9444

Posted by Sonit Singh Thursday 05 September 2024, 07:57:21 AM.

Hi Everyone,

A very warm welcome to COMP9444: Neural Networks and Deep Learning.

On behalf of COMP9444 teaching team, I am very pleased to have you for this term, and I hope you are going to find this course interesting and engaging as I do.

Neural Networks and Deep Learning plays a critical role in pushing the boundaries of what AI can achieve, making them indispensable for various industries and applications. Their ability to learn and adapt from data has revolutionized many fields and opened new opportunities for solving complex problems. This course provides an introduction to and deep exploration of neural networks and deep learning principles and practice.

This is Orientation Week (O-Week) and here are some suggestions for what you can do this week to ensure that you are well prepared for when the course starts.

• Familiarise yourself with the course structure and the assessment tasks – you can all the information in the course outline
• Start to think about how AI is encountered by you in your daily life and technologies underpinning those applications. Think about Apple’s FaceIDss, Voice assistants such as Siri, Alexa, or Cortana, smart home device, AI in banking, recommender systems on Netflix or ecommerce websites.
• Given the AI revolution is gathering even more pace and opening a new world of opportunities, think about what future look like and what does it mean for the future of work.
• Start to think about how you can embrace AI in your workplace and what benefits it can bring to the organization.

We will be using Ed discussion forum for any general questions. Please use the discussion forum on the Ed if you have any questions and teaching team will respond within 48 hours window.

For any specific question (having your personal information), please use class email: cs9444@cse.unsw.edu.au

The best way to get the most out of this course is to:

• Read through the materials and watch embedded videos before each lecture
• Complete quizzes and coding exercises
• Writing and running your own computer programs in PyTorch
• Asking questions and contribute to discussions on the Ed forum
• Consider further exploring topics of particular interest
• Participate in your tutorials (Week 1 to Week 5) and project mentoring sessions (Week 7 to Week 10)

Good luck with this course. I look forward to getting to know each of you and to a stimulating and productive learning journey!

With my very best wishes for a fabulous journey!

Regards,

Sonit Singh and COMP9444 teaching team