Advice for (prospective) students

Dear (prospective) ZHAW student, a very warm welcome to academia! You may just have entered the bachelor’s programme or “Fachstudium”, or you are already a mature engineer, ready to start your master’s or PhD studies - chances are you haven’t been exposed much yet to our setting of learning within an applied research and development context.

This blog post is meant to point you to a few (regularly updated) resources helpful in

shaping your mindset as being rooted in research and geared towards applications
giving you advice on how to aproach thesis work and writing in general
provides you with some pointer to additional study resources to major in the (attention, strong opinion) most interesting of all fields: artificial intelligence, in particular machine learning.

Here you go: you first may want to have a look at…

Lessons learnt from surviving a PhD by Andrej Karpathy (one possible role model of a successful student) on how to do Ph.D. studies is a very good read also for every undergraduate / graduate student: (a) it gives a very illuminating glimpse of how the research lab behind the lecturer works, and how the researchers (including the lecturer) think; (b) it has an incredibly well-written section on how to write a good paper (and, by derivation, a good thesis) -> if you just remember one thing, it’s “core contribution”; (c) it has a very good section on how to select an advisor (and how the advisor selects you) -> this is a lot about different styles that have to match well, just as in any relationship (for example, I tend to be very enthusiastic, but demanding).
Spinning Up as a Deep RL Researcher by Joshua Achiam on how to become a RL researcher; the post focuses thematically on deep reinforcement learning (and includes links to great introductory resources!), but gives important insights into what matters in entering the field of research (e.g., by means of a PA/BA/MT-thesis) methodology-wise for anyone interested in the experiment-driven areas of AI/ML.
PA_BA-Howto by Martin Braschler and myself on how to write a thesis at ZHAW; very good supplementary material to Karpathy’s post (in German).
Bewertungsschema_CH edited by myself, serving as my “Bauchgefühlobjektivierer” when it comes to grading theses and presentations (in German). If you have a careful look at the criteria and their weighting, you know exactly what matters (to me).
LaTex template for ZHAW thesis reports.
Standard outline for a student thesis in our field.

Related, here are a couple of links to blog posts of myself that pertain to successfully study, write a thesis, become an applied researcher, and by that start your career:

Some places to start learning AI & ML on where to find additional resorces to be educated in AI and machine learning. Includes links to very good MOOCs, to exciting blog posts with intuitive explanations and code for recent techniques and applications, and to more general advice on how to build a career as a data scientist out of this.
Doing applied science on what it means to do applied research, and an how to pick / limit down your research problem (“Forschungsfrage”) for e.g. your thesis project.
Great methodology ensures great theses on how to write a great thesis by showing great methodology to deal with arising problems.
Publish as you go on the (personal and organizational) importance of developing a mindset that is eager to publish valuable results. It is not about the publication; it is about pursuing things that are valuable potentially also to others, and about developing healthy self esteem that says “I can recognize if I did good work, and it will likely benefit others”. Additionally helpful is this post on how then to actually write the first draft of whatever you are going to publish.
Writing a draft on how to make best use of your co-authors in writing targeted darfts.
When to publish on how to find out if your work is worth publishing it to a broader audience, and what that should contain.
Getting to know oneself on getting to one’s own personal strengths and weaknesses that will have an impact on one’s career.
Scientific disciplines are foremost communities on how to navigate the academic landscape of subdisciplines, and ultimately choose one as your home base.
Career planning on how to not stop short in your career in order to make the most of your potential.
Science, applied (in German) on my concept of teaching the scientific method as the basis for good (machine learning) engineering and product development.

Written on September 9, 2016 (last modified: March 17, 2023)