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Tips for studying AI 200

AI 200 covers the mathematical foundations of AI. From an AI perspective, the topics in this course are very fundamental. However, from a high school student’s perspective, this may not be the case. In other words, the mathematical entry barrier to rigorously studying AI is not very low.

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We have seen many students work extremely hard in this course, yet become stuck or face challenges—especially in the linear algebra module. This is because linear algebra is a completely new branch of mathematics for most high schoolers. Its content and style of thinking are totally different from, and often unrelated to, what they learn in calculus. As a result, many students feel uncomfortable when encountering linear algebra for the first time.

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Students in this situation often ask themselves:

  1. Am I capable of continuing to take more advanced courses?

  2. Can I do well in AI Olympiads?

 

The answer to both questions is yes. Here are some tips:

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First, if you are stuck in AI 200—especially in the linear algebra portion—it is perfectly fine to pause and try another course that is less math-intensive, such as AI 210 or AI 310. Both courses are coding-focused. AI 210 focuses on NumPy, pandas, and matplotlib.pyplot, while AI 310 focuses on PyTorch. These do not require heavy mathematical background.

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Second, for machine learning and deep learning courses such as AI 300, it is true that they rely on much of the math taught in AI 200. However, once you see real applications of those mathematical tools, the concepts become much more intuitive. For example, AI 200 introduces singular value decomposition (SVD). Students may not initially understand why they are learning SVD. But when they later see how it is used in many machine learning algorithms, they often find that revisiting SVD in AI 200 becomes much easier.

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Learning is not a straight, upward path. The true learning path trends upward overall, but on a microscopic level, it moves back and forth like a zigzag.

AI 100 Markdown Programming for AI

  • Course contents

    • Text formatting

    • Writing code

    • Writing AI-related math

  • ​Prerequisite(s)

    • None​

  • Takeaways after completing this course​

    • Be ready to write solutions in paper-based competition (solutions shall be written on ipynb files, such as Google Colab text cells)​

    • Be ready to take

      • AI 200 Mathematical Methods for AI

  • Duration

    • 2 hours

  • FAQs​

Register

AI 200 Mathematical Methods for AI

  • Course contents

    • Linear algebra​

      • Space, subspace, basis, orthonormal vectors

      • Vector/matrix operations

      • Eigenvalues, eigenvectors

      • Matrix decompositions

    • Calculus

      • Single-variable derivatives

      • Multi-variable derivatives and gradients

      • Chain rule

    • Probability and statistics

      • Discrete distributions​

      • Continuous distributions

      • Mean

      • Variance, covariance

      • Bayes' rule

    • Convex optimization

      • Convexity​

      • Gradient descent

      • Duality

  • ​Prerequisite(s)

    • AI 100 Markdown Programming​ for AI

    • K-12 math course: Algebra 2

    • Prerequisite test: Click here

  • Takeaways after completing this course

    • Be on the halfway of earning Honor Rolls in USAAIO Round 1

    • Be ready to take​

      • AI 210 Coding for AI 1 - Advanced Python Techniques and Fundamental AI Libraries

  • Duration

    • 20 hours

  • FAQs

Register

AI 210 Coding for AI 1 - Advanced Python Techniques and Fundamental AI Libraries

  • Course contents

    • Advanced Python techniques for AI

    • NumPy

    • Pandas

    • Matplotlib

    • Seaborn

  • ​Prerequisite(s)

    • AI 100 Markdown Programming​ for AI​

    • AI 200 Mathematical Methods for AI

    • Prerequisite test: Click here

  • Takeaways after completing this course

    • Be ready to earn Honor Rolls in USAAIO Round 1

    • Be ready to take​

      • AI 300 Machine Learning 1

  • Duration

    • 20 hours

  • FAQs

Register

AI 300 Machine Learning 1

  • Course contents

    • Linear regression

    • Bias-variance trade-off

    • Regularization

    • Kernel methods

    • k-nearest neighbors

    • Cross validation

    • Logistics regression

  • ​Prerequisite(s)

    • AI 200 Mathematical Methods for AI

    • AI 210 Coding for AI 1 - Advanced Python Techniques and Fundamental AI Libraries

    • Prerequisite test: Click here

  • Takeaways after completing this course

    • Be on the half way of earning High Honor Rolls in USAAIO Round 1

    • Be ready to take​

      • AI 400 Machine Learning 2

  • Duration

    • 20 hours

  • FAQs

Register

AI 310 Coding for AI 2 - PyTorch

  • Course contents

    • Tensors

    • Autograd

    • Devices

    • Modules

    • Datasets

    • Dataloader, collation

    • Losses

    • Optimizers

  • ​Prerequisite(s)​

    • AI 210 Coding for AI 1 - Advanced Python Techniques and Fundamental AI Libraries

    • Prerequisite test: Click here

  • Takeaways after completing this course

    • Be ready to earn High Honor Rolls in USAAIO Round 1

    • Be ready to take​

      • AI 410 Deep Learning and Computer Vision 1

  • Duration

    • 20 hours

  • FAQs

Register

AI 400 Machine Learning 2

  • Course contents

    • Support vector machines

    • k-means clustering

    • Dimensionality reduction

    • Principal component analysis

    • Decision trees

    • Random forests

    • Boosting

  • ​Prerequisite(s)

    • AI 300 Machine Learning 1

  • Takeaways after completing this course

    • Be on the half way of earning Distinguished Honor Rolls in USAAIO Round 1

    • Be ready to take​

      • AI 410 Deep Learning and Computer Vision 1

  • Duration

    • 20 hours

Register

AI 410 Deep Learning and Computer Vision 1

  • Course contents

    • Multi-layer perceptron model

    • Forward propagation

    • Activation functions

    • Gradient descent

    • Adaptive moment estimation

    • Backpropagation

    • Parameter initialization

    • Dropout

    • Convolutional layers

    • Pooling layers

    • Batch normalization

    • Convolutional neural network

    • Image data augmentation

    • VGG

    • ResNet

    • GoogLeNet

    • Pretrained models

    • Transfer learning

    • Fine tuning

  • ​Prerequisite(s)

    • AI 300 Machine Learning 1

    • AI 310 Coding for AI 2 - PyTorch

    • Prerequisite test: Click here

  • Takeaways after completing this course

    • Be ready to earn Distinguished Honor Rolls in USAAIO Round 1

    • Be ready to take​

      • AI 500 Transformers

  • Duration

    • 20 hours

  • FAQs

Register

AI 500 Transformers

  • Course contents

    • Self-attention

    • Cross-attention

    • Masked self-attention

    • Layer normalization

    • Word embedding

    • Positional encoding

    • Inference

    • Training

    • Batch processing

    • Pre-training

    • Fine-tuning

    • BERT, T5, GPT

  • ​Prerequisite(s)

    • AI 400 Machine Learning 2

    • AI 410 Deep Learning and Computer Vision 1

  • Takeaways after completing this course

    • Be on the way of earning medals in USAAIO Round 2

    • Be on the way of potentially qualify for USAAIO training camp

    • Be on the way of potentially qualify for being on national team for international Olympiads

    • Be ready to take​

      • AI 510 Natural Language Processing and Graph Neural Networks

  • Duration

    • 20 hours

Register

AI 510 Natural Language Processing and Graph Neural Networks

  • Course contents

    • Character tokenization

    • Subword tokenization

    • Word tokenization

    • Word embedding method: Skip-gram

    • Word embedding method: Continuous bag of words

    • Word embedding method: Global vectors

    • Encoder-only transformers: BERT

    • Decoder-only transformers: GPT

    • Case study: IOAI contest problems

    • Message-passing neural networks

    • Graph convolutional networks

    • Graph attention networks

    • Vision transformers

  • ​Prerequisite(s)

    • AI 500 Transformers

  • Takeaways after completing this course

    • Be on the way of earning medals in USAAIO Round 2

    • Be on the way of potentially qualify for USAAIO training camp

    • Be on the way of potentially qualify for being on national team for international Olympiads

    • Be ready to take​

      • AI 520 Computer Vision 2 and Generative AI

  • Duration

    • 20 hours

Register

AI 520 Computer Vision 2 and Generative AI

  • Course contents

    • Contrastive language-Image pre-training

    • Adversarial attack

    • Object detection

    • Autoencoder

    • UNet

    • Variational autoencoder

    • Generative adversarial network

    • Denoising diffusion probabilistic method

    • Stable diffusion

    • Case study: IOAI contest problems

  • ​Prerequisite(s)

    • AI 510 Natural Language Processing and Graph Neural Networks

  • Takeaways after completing this course

    • Be ready to earn medals in USAAIO Round 2

    • Potentially qualify for USAAIO training camp

    • Potentially qualify for being on national team for international Olympiads

  • Duration

    • 20 hours

Register

AI 900 Grandmaster Course

  • Course contents

    • Advanced topics beyond all courses at 100-500 levels

  • ​Prerequisite(s)

    • All courses at 100-500 levels

  • Duration

    • 20 hours

  • Takeaways after completing this course

    • Qualify for being on your country's national training camp

    • Qualify for being on your national team to represent your country for IOAI and IAIO

    • Earn a medal (ideally a gold medal) in IOAI and IAIO

  • Eligibility to enroll in this course

    • Master all contents in 100-500 level courses

    • Have a strong established record

  • Registration​

    • Please contact us​

Register

FAQs for AI 100

Why is markdown programming the first course in your AI Olympiad curriculum?

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First, USAAIO has a paper-based component in its contest. It requires students to typeset math symbols and text responses using markdown programming, such as in text cells in Google Colab. Therefore, students need to learn how to use markdown programming.

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Second, for the coding-based component in USAAIO, students may need to annotate parts of their code, such as explaining how a snippet of code is derived mathematically. Students are required to add such annotations using markdown programming.

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Third, in our other courses, students are likely to ask instructors many questions. Markdown programming is an effective language for communicating about math and coding.

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FAQs for AI 200

This course teaches linear algebra. I have heard that it is a very hard subject to learn. What math background do I need to be ready for linear algebra?

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The only prerequisite is Algebra 2 from your K-12 math curriculum. We understand this answer may seem surprising, as many high schools do not offer linear algebra courses. Even in schools that do, students are often required to pass AP Calculus before enrolling in linear algebra. However, we stand by our answer that Algebra 2 provides a sufficient background. Here are the reasons:

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  1. Linear algebra does not involve calculus.
    There is no differentiation or integration in linear algebra, so a calculus background is not required.

  2. The perceived difficulty of linear algebra comes from two factors:

    • Abstract concepts and unique ways of thinking: Linear algebra problems require a different mindset compared to other math branches, such as geometry or calculus.

    • Lack of skilled instructors: Many instructors lack deep insight into linear algebra, making it challenging for them to teach the subject clearly and intuitively.

    Our course overcomes these challenges completely:

    • Intuitive teaching approach: While linear algebra belongs to algebra, many of its concepts and techniques can be better understood through geometry. This will be our primary teaching method.

    • Experienced instructors: Our instructors have several decades of experience teaching and applying linear algebra in diverse fields, such as mathematics, physics, computer science, engineering, and economics.

  3. Focused content:
    Our course does not cover every aspect of linear algebra. Instead, we focus solely on the topics needed for AI, ensuring a streamlined and relevant learning experience.

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This course has a calculus component. Does that mean the prerequisite is AP Calculus AB/BC?

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No.

  1. We do not cover all topics in calculus.
    The course focuses only on the concepts used in AI, such as the chain rule for computing derivatives. Many topics covered in AP Calculus AB/BC, such as solving differential equations and performing complicated integrals, are beyond the scope of this course.

  2. We start from the fundamentals.
    The course begins with the basic concepts of calculus, so students who have never studied calculus before can easily follow along.

  3. Advanced content for those with prior calculus experience.
    Students who have already taken AP Calculus AB/BC will still learn new material, such as computing partial derivatives in multivariable calculus and using Lagrangian duality to solve continuous-variable constrained optimization problems.

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What is the relationship between this course and math Olympiad courses, such as AMC/AIME training courses? Should I take AMC math competition prior to taking this course?

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This course and math Olympiad training courses are completely different. The mathematical knowledge and skills required for AI are very different from those tested in math Olympiads, such as AMC, AIME, USAMO, and IMO.

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For example, calculus and linear algebra, which are fundamental to studying AI, are not tested in math Olympiads.

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Therefore, if a student has never participated in math Olympiads or their related training courses, it does not disadvantage them in taking this course. On the other hand, for students who have been involved in math Olympiads, this course is still valuable as it teaches a completely different set of mathematical knowledge and skills specifically tailored for AI.

FAQs for AI 210

The prerequisite for this course is that students need to know the fundamentals of Python. However, this description is too vague. What exactly does "fundamentals" mean?

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When we say "fundamental," we mean that you only need to know the basics of Python before taking this course. There is no requirement to be familiar with advanced topics or techniques in Python. To be more specific, if you can understand the topics and answer questions from the following chapters in the W3Schools Python Tutorial, you are well-prepared to enroll in our class:

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By understanding these topics, you will be fully prepared to succeed in our course.

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What is the relationship between this course and USACO training courses?

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Although both USAAIO and USACO involve coding tasks, these two Olympiads require completely different coding skills.

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First, all coding tasks in USAAIO must be completed using Python. By contrast, in USACO, when you advance to higher divisions, such as Gold, you typically need to use C++.

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Second, in USAAIO, you need to know how to work with AI-related libraries in Python, such as NumPy, pandas, matplotlib, and seaborn. However, these libraries are not part of USACO.

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Therefore, enrolling in this course does not require any prior experience with USACO. For those who have participated in USACO, you can still take this course as long as you are familiar with the basics of Python.

 

Additionally, this course is valuable even for students with USACO experience because the AI-focused libraries and coding skills taught here are not covered in USACO but are essential for success in the AI Olympiad.

FAQs for AI 300

What prerequisites are required prior to taking AI 300?

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Students need to know AI 200 and AI 210 prior to enrolling in AI 300. 

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First, AI 300 assumes that students have necessary mathematical backgrounds, such as linear algebra, optimization and probability. In AI 300, we treat machine learning models in a rigorous way. For instance, in the linear regression model, we comprehensively use linear algebra and optimization to derive the formula of the mean-squared error estimator. Students who lack necessary math backgrounds will be lost even in our first class.

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Second, AI 300 assumes that students have necessary coding background, particularly NumPy. For instance, we need to make a variety of manipulations of multi-dimensional NumPy arrays, such as swapping two dimensions, reshaping an array, and doing broadcasting. Students are required to program every machine learning model from scratch by using NumPy. Therefore, NumPy is a prerequisite.

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For those machine learning models covered in AI 300, do students learn how to mathematically derive them, or how to program them?

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Both are required. The rule of thumb is that we start from the first principle. For instance, while teaching students logistic regression model, we start from teaching students how to formulate the problem as a mathematical optimization problem. To use gradient descent algorithm to solve it, we teach students to derive its gradients. After completing these math tasks, we then use these results to program from scratch to build a logistic regression model.

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Do I need to know Sklearn prior to taking AI 300?

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No. This is not a prerequisite.

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First, our course emphasizes on the first principle that students need to build all machine learning models from scratch without using any high-level API, such as Sklearn, that students can use without even knowing the mechanism behind those models.

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Second, in AI 300, after teaching each model, we will show students the implementation of that model in Sklearn. This allows us to check results generated from both our own model and Sklearn to ensure that our model is correct. In this step, students can quickly learn how to use Sklearn.

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Is AI 300 a prerequisite of taking any deep learning course?

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The answer is mixed, both yes and no.

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The reason of saying "yes" is as follows.

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If you have sufficient time to prepare for AI Olympiads (at least half a year), we recommend you to take AI 300 prior to taking deep learning courses.

 

First, some methods and concepts in deep learning are learned in AI 300, such as cross entropy, overfitting.

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Second, AI 300 is a good chance to improve student's skills of using math and NumPy to do AI.

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The reason of saying "no" is as follows.

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First, many topics in deep learning are not based on classical machine learning models. Second, most deep learning models require students to program in PyTorch (covered in AI 310), not NumPy.

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To summarize, if you have at least half of year to prepare for AI Olympiads, we suggest you to learn AI 300 and deep learning courses (such as AI 410) in order. Otherwise, if time is too short for you, then you may consider to take both AI 300 and deep learning courses together.

FAQs for AI 310

What prerequisites are required prior to taking AI 310?

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Students need to know AI 210 prior to enrolling in AI 310. Reasons are as follows.

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1. Tensors resemble NumPy arrays
PyTorch tensors are modeled after NumPy arrays. Learning NumPy first makes PyTorch’s tensor operations intuitive and familiar.

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2. Matching syntax and broadcasting
PyTorch adopts NumPy-like syntax for operations like slicing, reshaping, and broadcasting. This reduces the learning curve dramatically.

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3. Data preprocessing depends on NumPy
Most raw data is loaded and cleaned using NumPy or Pandas before being converted to PyTorch tensors. You need NumPy to prepare data for model input.

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4. Debugging with NumPy
NumPy helps verify PyTorch computations by serving as a trusted reference. You can use it to double-check shapes, math, and expected outputs.

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Is AI 310 a prerequisite of taking any deep learning course?

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Yes.

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AI 310 is about PyTorch programming. All deep learning models in subsequent courses use PyTorch.

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Is GPU needed for AI 310?

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Yes. You may need to move tensors to GPUs to accelerate your computation.

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Both PyTorch and TensorFlow are used in deep learning. Why AI 310 teaches PyTorch, not TensorFlow?

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First, all AI Olympiads, such as USAAIO, IOAI, used PyTorch, not TensorFlow.

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Second, in many university deep learning courses and research, students are required to use PyTorch to do homework and projects.

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Third, PyTorch has better debugging experience.

FAQs for AI 410

Why AI 300 is a prerequisite of AI 410?​​​

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1. Deep Learning is a Subset of Machine Learning

  • Machine learning is the broader field: algorithms that learn patterns from data (e.g., linear regression, decision trees, SVMs, clustering).

  • Deep learning is a special class within ML that uses neural networks with many layers.

  • Without the ML foundations, DL can feel like a black box.

 

2. You Need the Core Concepts

Before deep learning, you should be comfortable with:

  • Supervised vs. unsupervised learning

  • Overfitting & underfitting

  • Bias–variance tradeoff

  • Training vs. test data, cross-validation

  • Evaluation metrics (accuracy, precision, recall, F1, etc.)

Deep learning models still face these same issues — just at a larger scale.

 

3. Understanding Optimization

ML introduces concepts like:

  • Gradient descent

  • Loss functions

  • Regularization (L1, L2, dropout analogies)

These are the same mathematical ideas that DL builds upon — only with bigger networks and more parameters.

 

4. Data Preprocessing Skills

Classical ML teaches you:

  • Feature scaling, normalization

  • Feature engineering

  • Handling missing data

In DL, raw data is more common (images, audio, text), but preprocessing and understanding feature distributions are still critical.

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Why AI 310 is a prerequisite of AI 410?​​​​

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1. Deep Learning ≠ Just Theory

  • Deep learning isn’t only about math and concepts; you need to implement and experiment.

  • PyTorch is the main framework researchers and practitioners use to build, train, and test neural networks.

  • Without it (or an equivalent like TensorFlow/JAX), you’d be stuck coding all backpropagation, matrix ops, and optimization from scratch — which is impractical.

 

2. PyTorch Handles the Heavy Lifting

  • Automatic differentiation (autograd) → you don’t manually compute gradients.

  • GPU acceleration → efficient large-scale training with CUDA.

  • Optimizers, loss functions, layers → all pre-built.
    This frees you to focus on model design and research ideas, not low-level math implementation.

 

3. AI Olympiads, Industry, Research Standard

  • USAAIO, IOAI and many other national and regional AI Olympiads require students to use PyTorch to solve deep learning problems.

  • Most academic papers, open-source projects, and tutorials use PyTorch.

  • Hugging Face (transformers, diffusers), OpenAI, Meta, and many labs release code in PyTorch.

  • If you want to reproduce results, extend papers, or collaborate, PyTorch literacy is expected.

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Is GPU needed for AI 310?

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Yes. You may need to move tensors and models to GPUs to accelerate your computation.​

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