What is a good learning pace​

 

Although 10 courses are all simultaneously offered, every student/parent shall make your own decision (we definitely welcome you to consult us if you have any question) how many courses you plan to take in each quarter. A reasonable study plan is to complete all courses in two years. Therefore, a reasonable workload for majority is 1-2 courses per quarter.

 

​If your learning pace is 1 course per quarter, you will finish our curriculum in 2 years.

 

If your want to speed up and try 2 courses per quarter, you will finish our curriculum in 1 year.​

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What are prerequisites before enrolling into your courses​

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For AI 100 Markdown Programming, there is no prerequisite. Everyone can take it.

 

To enroll into our 200-level courses (AI 200 and AI 210), we require students to know at least Algebra 2 and Python.

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To enroll into other courses, please refer to this page to learn about the prerequisite for each course.

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What is a good learning path​

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First, if you do not know how to do markdown programming (writing math in LaTeX), we recommend you to begin with AI 100. If you already know how to do it, you may skip this course.

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Second, after knowing how to do markdown programming and by knowing Algebra 2 and Python, you can take AI 200 and AI 210. These courses help you build a solid math and coding foundation for learning AI.

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Third, with foundations in math and coding (after taking AI 200 and 210), you can start your journey to study machining learning. There are two machine learning courses, AI 300 and 400. You shall take AI 300 first. After that, you take AI 400.

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Fourth, after taking AI 210 (Coding for AI 1), you can take AI 310 (Coding for AI 2). This course teaches you PyTorch, the coding tool for deep learning. There is no relationship between AI 300 and AI 310. So you may take these courses at the same time.

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Fifth, after taking AI 300 and AI 310, you are ready for your study in deep learning. The first course of deep learning is AI 410.

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Sixth, AI 410 opens the door for you to study deep learning courses. After taking AI 410, you shall take AI 500. This course teaches you transformers model. This is a core model in modern large language models, computer vision models and generative AI.

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Seventh, after taking AI 500, you can take AI 510 and AI 520 together. These two courses are about advanced topics in deep learning.

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Eighth, after taking above courses, you may consider our grandmaster course AI 900. This course prepares you to get into a national camp, national team, and finally get a medal in an international competition, such as IOAI or IAIO.

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My kid knows Python, but her/his math level does not reach Algebra 2 yet. Is it too early for her/him to take your courses?​

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This is quite typical for many middle school or even primary school students. Our recommendations are as follows.

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First, you can always take AI 100. It has no prerequisite.

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Second, after taking AI 100, you may take AI 210. This is a coding course to prepare you to program for machine learning tasks. It does not require too much math.

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Third, after taking AI 210, you may take AI 310. This is a coding course to prepare you to program for deep learning tasks. It also does not require too much math.

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Fourth, after taking AI 210, you may take AI 300, our first machine learning course. However, since you do not have sufficient math background, you may find many topics very hard to follow. This is totally fine. You may skip those math components and focus on (1) understanding the intuition behind those machine learning models, (2) programming with those machine learning models.

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Fifth, after taking AI 300 and AI 310, you may take our deep learning sequence courses AI 410, AI 500, AI 510, AI 520. Same advice as above, please skip those parts with heavy math and focus on understanding the intuition and implementation in coding.

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Sixth, when you take more math course, you may go back to revisit math components in our courses.

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AI 200 is too hard for me, particularly the linear algebra module. Should I stop taking other courses and even give up AI Olympiads?​

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NO!!!

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So far as what we know, AI 200 is perhaps the most difficult course in our curriculum, although it provides foundational math tools for AI. This is because linear algebra is relatively more abstract compared to other math branches, such as calculus.

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If you are stuck in AI 200, please follow our advice to the above question that you continue to move forward to our higher level courses and put your focus on the intuition and programming of AI models.