- Keep spelling and grammar in mind; it’s one of the first thing reviewers notice.
- Show that you are committed to learning more about machine learning. For example, if you’ve taken an online course (such as Andrew Ng’s Coursera course) you should mention it (and if you haven’t we would highly recommend doing so).
- Talk about how you are already using, or how you plan to use, machine learning for your work or research.
- Talk about how you are contributing to your local machine learning community. For example, do you organize meetup events, run lectures, or lead a study group?
- Make sure you put plenty of effort into the application since this is one way for us to judge your interest in attending.
Ignore the odds & competition.
Deliver the best you got. Inquire if you’ve got questions. Don’t take rejection personally. Request feedback, apply again. You want in? You’ll make it eventually. We encourage you to apply for one of the many other Indaba𝕏 events around the continent or to the main Indaba event, and, to keep an eye open for next year’s Indaba𝕏 ZA.
Don’t disqualify yourself
We see LOTS of applications, way more than we can take. While you disobey instructions, leave fields blank, or are being flippant, others are crushing the application. Does your application look like you care 80%? Superstar with 8 NeurIPS papers, but too cool to complete the application properly? You are too cool for this Indaba𝕏 then.
Present yourself well
Assume the people evaluating your application don’t know you, your schools, your prizes. They’ll go by how YOU present yourself. Explain exhaustively, be real, don’t brag, state matter-of-factly how great you are (because you are!). Examples of your motivation will help more than external validation.
Use the space you got
Obey the word limits on your answers strictly. If we ask for background in 200 words, don’t write “machine learning” (see point Don’t disqualify yourself). We assess if you’re a good fit on what you provide.
Plagiarise and lie at your peril.
Each year we see apps which underestimate our ability to Google, or we detect strange variations in style and quality. Enthusiastically admitting your ignorance (e.g. on your experience and what you hope to gain) will be positively noted. Impostering? Not so much.
What would you gain from the IndabaX conference? [200 words]
Candidate A (poor):
I would have lots of fun and learn a lot.
Candidate B (good) [202 words]:
The IndabaX provides an opportunity to learn about a variety of ML theory and applications that I am not exposed to every day – providing ample learning opportunity. It is also an excellent opportunity to learn from experts in the field, as well as network and engage with people with a similar passion for this awesome field. Machine learning is an integral part of my MSc research in that I am using machine learning (ML) to find new patterns in the EEG signals that can help develop our understanding of the brain. At previous events (IndabaX and DL Indaba 2018) I have learnt so much and had the opportunity to ask questions directly related to my research, giving me an invaluable advantage in figuring things out to advance my MSc research to a new level. Another opportunity to advance in my research would be extremely appreciated as ML is also an invaluable skill that I would like to develop beyond my MSc, as I believe there are many advantages to it – especially in Health-related fields are immense and many people can benefit from the merging of medicine and technology (especially ML). I intend to be a part of building that bridge.
Candidate C (good) [197 words]:
Generally I would like to take every opportunity to further my participation in the local and global machine learning communities as this is my chosen career path. As a medical doctor currently carrying out a coursework masters in computer science, I need to establish myself in this new professional network of academics and industry practitioners if I hope to effectively contribute to research in this area. Understanding which open questions in a field are relevant is not an easy task. I feel the best way to scope this out is to engage with individuals with various backgrounds and interests and to hear the conversations and arguments between those who are more established. Specifically, a couple of days of high-intensity learning tackling material that is part of my coursework but doing so from a different perspective could be extremely beneficial to my holistic understanding. Good practice for community management is also at the top of my mind as I am supervising individuals organising the World Health Science Students Symposium in Cape Town in December (https://www.whss2019.com/) and I hope to pick up some elements of how the Indaba is run and how continued participation post Indaba is carried out.
What would you bring to the IndabaX? [200 words]
Candidate A (poor):
Candidate B (good) [191 words]:
Here are a few things I would bring to the deep learning indaba:
- Enthusiasm: I am relatively new to the field of machine learning and I am excited by learning new things. I will, therefore, be very excited about most things there, possibly enabling me to do more that an unenthused person.
- Hard work: I am a hardworking person and I try very hard to understand how things work, so I will try my best to learn from all the experts there, and help where I can.
- Skills: I have some skills in developing systems, solving problems and designing algorithms and I would be willing to share this with anyone who can benefit from it.
- Perspective: I might be able to offer a different perspective than others, as I understand some problems that my father, as a CFO of a large company, has. These problems, coming from someone that is not an expert in machine learning might offer a useful perspective. Also, my sometimes beginner questions can potentially give people a better understanding of the concepts, or even make them see it in a different, better way.
Candidate C (good):
I have a degree in mathematics, and have a thorough understanding of mathematical concepts and application. I am proficient in python for scientific computing. I have some experience in TensorFlow and currently learning more through an RL project. I have implemented and played around with some deep learning architectures. With this knowledge, I hope to help anyone struggling with mathematical concepts and intuition and some python programming. I am friendly and enjoy talking to all types of people coming from various backgrounds, listening and understanding their ideas and perspectives. I hope that I can interact with people in a way that allows them to feel comfortable and get excited and involved. I also like to stay updated and connected in my university and with other universities and groups. I am currently involved in an RL group with Person X and Person Y and working on a project trying to implement ML for stock portfolios for an investment company (Money McMoneyface). I study at AIMS which has a thriving deep learning research community of which Keynote Speaker is a part of. With all this knowledge and connections I hope to help others get connected with relevant people and resources to further their research and opportunities for collaborations. [206 words]
Describe your research or experience [200 words]
Candidate A (poor):
Candidate B (good) [185 words]:
I have done a year long undergraduate level research project. The project was a Bifurcation analysis on the Hodgkin Huxley model, I mostly used Python and matcont in Matlab for some bifurcation visualisations. I also worked as a research assistant for a machine learning research project for Automated identification of tsetse fly wing vein intersections. My current research is In deep learning, my research project is still being decided but a number of potential topics include Deep learning in complex dynamical systems, Image reconstruction from EEG signals using convolutional neural networks, Creating intelligent agents with reinforcement learning . Other Academic experiences of mine have been competing in the MCM applied math competition in Stellenbosch where we created a Bayesian approach to calculating student marks. I have also been to the IBRO Simons computational neuroscience camp. I have worked on some stock portfolio data, applying mean variance optimisation, but I am currently busy with this. I am currently studying so my experience outside of studies is not extensive but I have completed a degree in mathematics and I am currently busy with an honours in biomathematics."