Preparing for AI Graduate School A-Z: From Spec Preparation to Contacting Professors, Application Documents, and Interviews

How to prepare for AI graduate school? 🤔

Preparing for graduate school in AI is a long process that carries uncertainty.

There are many things to prepare, but I don't know if I'm doing it right. Some people have the experience of having difficulty concentrating on their studies/work because they worry, "If I do this, will I be able to pass...?" This period can last from a few months to a year. How can I spend this period well? How can I spend this period well and achieve the result of "graduate school admission"?

The 'successful candidates' answers to the above questions are included in this lecture.

I will tell you how to prepare for the entire process, from lab contact to document evaluation and interview evaluation. I analyzed my experience preparing for graduate school and the experiences of people who were accepted to graduate school around me. I will reduce the uncertainty that students may feel.

The features of the lecture are as follows:

💪 The lecture is structured in a ‘practical way’.

We have eliminated all the difficult and hard to implement methods. We have included simple but easy to implement methods that will increase your chances of passing.

📚 The course includes core 'entrance exam materials'.

• Contact Email Format

• Sample self-introduction

• CV format

• 100 Interview/Interview Questions

• Draft letter of recommendation

• Advance information for interview preparation

These are materials that you may need to purchase separately from other sites. Or, you may not need to purchase them, but it may be difficult to know whether they are reliable. We have prepared materials from reliable sources so that you can receive them all at once!

Let's take a closer look at the top 100 interview/interview questions.

100 Interview Questions Sample Questions

Linear Algebra

🧱 Explain eigenvalues and eigenvectors.

🧱 Explain PCA(Principal Component Analysis) mathematically.

Probability and Statistics

🧱 Explain the central limit theorem. 🧱 Explain the Bayesian theorem and express it in a formula.

Machine Learning

🧱 What is overfitting and how can we fix it? 🧱 Why is a high margin good in SVM?

Data Structure Algorithms and Coding

🧱 Explain Quick Sort. 🧱 What is a Heap?

etc

🧱 ~To be honest, it's not the best school, but is there a reason you went there? (Question asked during the interview)

🧱 My grades in ~ subjects are not good. Can you tell me why?

I recommend this to these people

Who created this course

• Position

  
  

  • Master's Program in Artificial Intelligence

• Paper

  • Standard Layer Addition Methods in Hierarchical Reinforcement Learning: Timely Hierarchical Elaborated FeUdal Networks

  • HierarchyDrop: Dynamic Hierarchical Reinforcement Learning for Long- and Short-Term Subgoals

• Others

  • Artificial Intelligence Club Operation (2022~2023)

  • Conducted numerous AI-related mentoring and tutoring (machine learning, deep learning, graduate school preparation, etc.)

  • Running multiple studies (deep learning, natural language processing, databases, computer vision, reinforcement learning, etc.)

Things to note before taking the class