AI Introduction Study Method by a Graduate Student Who Wrote 2 Papers as an Undergraduate
epoch
Introducing AI entry-level study methods for undergraduates.
入門
AI, Linear Algebra, Probability and Statistics
Through this lecture, you'll be able to learn about the AI graduate school admission process and preparation methods from start to finish! I'll also share some great tips to increase your chances of getting accepted!
Creating a CV
Writing a contact email
Interview preparation
Writing a Self-Introduction Essay
Atmosphere of the actual interview and how the interview proceeds
Interview preparation
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:
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.
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!
💡 Top 100 Interview/Interview Questions
We provide a questionnaire to use in preparing for your interview/interview.
Includes questions on 'Linear Algebra', 'Probability and Statistics', 'Machine Learning', 'Data Structure Algorithms and Coding', and 'Others' . Each consists of 20 core questions.
We provide sample answers . They will serve as important milestones for you to evaluate your answers. They will help you fill in the gaps in your answers and correct any misconceptions.
We provide both materials with sample answers and materials that only contain the questions. This will allow you to practice answering the questions on your own without being distracted by the sample answers.
Sample questions are given below. Sample answers are also included in the materials provided in this course.
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?

Undergraduate students hoping to advance to graduate school in artificial intelligence
Undergraduate students preparing for graduate school entrance exams in artificial intelligence or those who want to build up their qualifications for the exam

Working people who want to go to graduate school in artificial intelligence
Working professionals who want to advance to graduate school to change fields with artificial intelligence

undergraduate graduate
College graduates with a related or unrelated major
💡 Graduates or working professionals with little to do with AI may find it difficult to start preparing. If you leave a question, we will think about it together according to your situation! 😃
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.)
We provide it as a pdf file. You can download it and use it right away.
No prior knowledge is required. Just follow the content of the class.
💡 If you explain your situation in detail during the consultation, the quality of the answer I can give you will improve!
Experience with artificial intelligence
Experiences outside of AI
It is necessary because it can be used as an important appeal point in entrance exams.
etc
You can appeal with qualifications, etc., but you also need non-main content (refer to the first lecture for distinction).
Everyone's situation is a little different. I'll give you what I can advise. Please refer to the above and ask questions!
Who is this course right for?
For those who don't have seniors to help with their AI graduate school application
For those who don't want to rely solely on internet information to prepare for AI graduate school.
For those who want to hear and learn from detailed experiences of successful AI graduate school admissions.
For those who want to obtain a lot of information about graduate school admissions for artificial intelligence
Need to know before starting?
No prior knowledge is needed.
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Answers
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Courses
안녕하세요.
강의하는 대학원생 에폭입니다.
인공지능/대학원과 관련한 주제로 여러분과 소통하고 있습니다.
__________
Position
인공지능 대학원 석사과정
Paper
계층적 강화학습에서의 표준적 계층 추가 방안: Timely Hierarchical Elaborated FeUdal Networks
HierarchyDrop: Dynamic Hierarchical Reinforcement Learning for Long- and Short -Term Subgoals
Others
인공지능 동아리 운영(2022~2023)
다수의 인공지능 관련 멘토링 및 과외 수행(머신러닝, 딥러닝, 대학원 준비 등)
다수의 스터디 운영(딥러닝, 자연어처리, 데이터베이스, 컴퓨터비전, 강화학습 등)
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12 lectures ∙ (1hr 52min)
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$29.70
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