top of page

AI Courses

Tufts offer.png

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 20J Conceptual Linear Algebra for AI:
An Intuitive, Visual, and Beginner-Friendly Approach

  • Course contents

    • Vectors

    • Inner product and similarity

    • Outer product

    • Vector space

    • Basis vectors

    • Matrices

    • Matrix multiplication

    • Linear transformation

    • Eigenvalues and eigenvectors

    • Singular value decomposition

  • ​Prerequisite(s)​

    • K-12 math course: Algebra 1, Geometry

  • Takeaways after completing this course

    • Be ready to take AI 200 - Mathematical Methods for AI

  • 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

    • Decision trees

    • Random forest

    • Boosting

    • Dimensionality reduction

    • Principal component analysis

    • t-SNE

    • UMAP

    • k-means clustering

    • Semi-supervised learning

    • Time-series analysis

  • ​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

  • FAQs

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

  • FAQs

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

  • FAQs

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?

​

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.

​

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.

​

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.

​

FAQs for AI 20J

Why do we study linear algebra for AI?

 

Linear algebra is the mathematical language of artificial intelligence. Almost all modern AI models represent data as vectors and matrices, and neural networks are built using matrix operations. When AI learns from data and makes predictions, it is performing linear algebra operations.

Linear algebra is used in many AI areas, including:

​

  • Image representation and image compression

  • Word embeddings and text representation

  • Recommendation systems

  • Neural networks and deep learning

  • Transformers and modern AI models

This is why linear algebra is considered the foundation of AI.

 

Linear algebra sounds like an advanced college subject. Why are Algebra 1 and Geometry enough background?

​

This course teaches linear algebra in a conceptual and geometric way rather than in a traditional abstract way. We focus on understanding ideas visually and geometrically before introducing formulas.

Students mainly need:

​

  • Algebra 1 (equations, variables, basic operations)

  • Geometry (coordinates, slope, distance, shapes)

  • Willingness to think visually and conceptually

 

We use 2D and 3D geometry, visualization, and simple arithmetic to explain linear algebra concepts, so advanced math is not required.

 

Does this course require coding?

​

No. This course does not require coding. This is a concept-based course that focuses on visualization, geometric understanding, and the mathematical ideas behind AI.

Students may see some simple computations, but they can be done:

​

  • By hand

  • Using basic algebra

  • Using geometry and visualization

 

Coding will be introduced in later courses such as AI 210.

 

What is a conceptual approach to linear algebra?

 

A conceptual approach means that students focus on understanding the meaning behind the mathematics rather than memorizing formulas or performing long calculations. Students learn what vectors, matrices, projections, and transformations mean geometrically and how they are used in AI.

In a conceptual approach:

​

  • Students focus on understanding ideas

  • Students learn through geometry and visualization

  • Students see how the math is used in real AI problems

  • Students understand why the formulas work, not just how to use them

 

This makes linear algebra much easier to understand and prepares students for more advanced AI and mathematics courses later.

 

What does intuitive and visual mean in this course?

​

Intuitive and visual mean that students learn ideas through pictures, geometry, and visualization before learning formulas. For example:

​

  • A vector is understood as an arrow with direction and length

  • A matrix is understood as a transformation such as rotation, stretching, or projection

  • The dot product is understood as a measure of similarity

  • Projection is understood as a shadow or approximation

  • Linear combination is understood as mixing several basic vectors together

 

Students understand the idea first, then learn the formula, and then see how it is used in AI.

 

I am a middle school student. Can I take this course?

​

Yes. Motivated middle school students can take this course. The recommended background includes:

​

  • Algebra 1

  • Basic geometry (coordinates, slope, distance)

 

This course focuses more on understanding ideas and visualization rather than heavy algebra or complicated calculations.

​

What is the fundamental difference between this course and a university-level linear algebra course?

​

University linear algebra courses are usually designed for math, engineering, or science majors and often focus on theory and proofs. This course focuses on conceptual understanding, visualization, and AI applications.

This course:

​

  • Focuses on geometric understanding

  • Uses visualization and animation

  • Connects linear algebra to AI applications

  • Emphasizes understanding rather than formal proofs

  • Is designed for middle school and high school students

 

This course is about linear algebra and applications. Are the applications related to AI?

​

Yes. All applications in this course are related to AI and data science. Students will see applications such as:

​

  • Image representation

  • Image compression

  • Search engines and ranking

  • Recommendation systems

  • Face recognition

  • Word embeddings

  • Neural networks

  • Transformers

  • Dimensionality reduction

 

What is the relationship between AI 20J and AI 200?

​

AI 20J is the mathematical foundation course, and AI 200 is the machine learning course. AI 20J teaches:

​

  • Vectors

  • Matrices

  • Projection

  • Similarity

  • Geometric understanding of data

 

AI 200 teaches:

​

  • Linear regression

  • Logistic regression

  • Clustering

  • Principal component analysis

  • Neural networks

 

AI 20J teaches the mathematical language of AI, and AI 200 teaches how to build AI models using that language.

 

Do students need to be strong in math before taking this course?

​

No. This course is designed so that students learn math through visualization, AI examples, and geometric understanding. The course emphasizes understanding concepts rather than memorizing formulas.

 

After finishing this course, what should students expect to gain?

​

After finishing this course, students will:

​

  • Have a strong conceptual understanding of linear algebra

  • Understand how linear algebra is used in AI and machine learning

  • Be comfortable with vectors, matrices, projection, similarity, and transformations

  • Be well prepared for AI 200 and future AI courses

  • Have an advantage when taking more rigorous math or AI courses later

 

Students who understand concepts first usually find later courses much easier.

 

Is this course more theoretical or more practical?

​

This course is conceptual and application-oriented. It focuses on understanding ideas and how they are used in AI rather than on proofs or heavy calculations. In this course:

​

  • We use simple mathematics to explain important ideas

  • We focus on concepts, intuition, and visualization

  • We explain how the math is used in real AI applications

  • We do not require coding

 

The course prepares students for more advanced AI and machine learning courses in the future.

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?

​

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:

​

  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.

​​​

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

​

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.

​

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?

​

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.

​

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

​

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?

​​

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:

​

 

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

​​

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

​

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

​

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++.

​

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.

​

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?

​​​

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

​

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.

​

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.

S

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

​​​

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.

​

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

​​​

No. This is not a prerequisite.

​

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.

​

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.

​

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

​​​

The answer is mixed, both yes and no.

​

The reason of saying "yes" is as follows.

​

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.

​

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

​

The reason of saying "no" is as follows.

​

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.

​

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?

​​​

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

​​

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

​

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

​

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.

​

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.

​​

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

​​​

Yes.

​

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

​​​

Is GPU needed for AI 310?

​​​

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

​

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

​​​

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

​

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

​

Third, PyTorch has better debugging experience.

FAQs for AI 400

What prerequisites are required prior to taking AI 400?

​​​​

Students are expected to complete AI 300 before enrolling in AI 400.

​

The AI 300 course provides the fundamental foundations of machine learning, which are necessary for understanding the advanced topics covered in AI 400. In particular, AI 300 introduces students to key concepts such as:

​

  • Loss functions and optimization objectives

  • Basic machine learning models

  • Model training and evaluation methods

  • Performance metrics and validation techniques

 

These topics establish the core understanding required to study more advanced machine learning methods.

In AI 400, the curriculum moves to more advanced machine learning topics, and therefore assumes that students are already familiar with these fundamental concepts. Without this background, it would be difficult to follow the theoretical and practical material covered in the course.

​

For this reason, AI 300 is a required prerequisite for AI 400.

​​

Do I need to use a GPU for this course?

 

​​​​No. A GPU is not required for this course.

​

This course focuses on classical machine learning methods rather than deep learning models. As a result, the computational requirements are relatively modest, and all programming and computation tasks can be performed using a standard CPU.

​

Students will primarily work with algorithms and techniques such as:

  • classical machine learning models

  • loss functions and optimization methods

  • model evaluation and validation techniques

 

These methods are computationally efficient and do not require GPU acceleration. Therefore, students can complete all assignments and projects using a regular personal computer with a CPU.​

FAQs for AI 410

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

​

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.

​​​

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

​

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.

​​​

Is GPU needed for AI 310?

​​​

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

FAQs for AI 500

Why AI 410 is a prerequisite of AI 500?​​​​

​​

AI 410 is the foundational deep learning course, while AI 500 focuses on Transformers, which are advanced deep learning architectures. Therefore, students must first understand the core principles of deep learning before studying Transformer models.

​

In AI 410, students learn the fundamental concepts that modern deep learning models are built upon, including:

  • Multilayer Perceptrons (MLP) and neural network architectures

  • Backpropagation and gradient-based optimization

  • Training dynamics of deep neural networks

  • Techniques such as Batch Normalization, and other stabilization methods

 

These concepts are essential because Transformer models are fundamentally deep neural networks composed of multiple layers of neural modules. For example:

  • Transformer architectures include feedforward neural networks (MLPs) inside each layer.

  • Training large neural networks requires understanding normalization techniques, which are critical for stabilizing Transformer training.

  • Many design choices in Transformers rely on the same optimization principles and training strategies used in deep learning.

 

Therefore, AI 410 provides the theoretical and practical foundation needed to understand how Transformer models work and how they are trained.

 

For this reason, AI 410 is required before taking AI 500.

​

Why is Transformers taught as a dedicated course (AI 500)? Is it really that important?

 

Yes. Transformers are one of the most important model architectures in modern artificial intelligence, which is why they are taught as a dedicated course.

Since their introduction in 2017, Transformer models have become the dominant architecture across many areas of AI, including:

  • Natural Language Processing (NLP) — models such as BERT, GPT, and other large language models are all based on the Transformer architecture.

  • Computer Vision — Vision Transformers (ViT) and related models are widely used for image recognition and other vision tasks.

  • Multimodal AI — systems that combine text, images, audio, and video often rely on Transformer-based architectures.

  • Generative AI — many modern generative systems are built using Transformer-based models.

 

Because Transformers form the core architecture behind many state-of-the-art AI systems, understanding them has become a key skill for students who want to study advanced AI or participate in AI competitions and research.

​

The AI 500 course focuses entirely on Transformers so that students can:

  • understand the attention mechanism, which is the central idea behind Transformers

  • learn how Transformer models are structured and trained

  • study how Transformers are applied across different domains of AI

 

Given their central role in modern AI, Transformers deserve focused and in-depth study, which is why they are offered as a dedicated course.

​​​​

Are there enough topics in Transformers to span an entire course?

 

Yes. Transformer models involve a wide range of important concepts and techniques, making them rich enough to support an entire course.

​

Although the Transformer architecture may appear simple at first glance, it combines many key ideas in modern machine learning and deep learning. A comprehensive study of Transformers typically includes topics such as:

  • Attention mechanisms, including the intuition and mathematics behind self-attention

  • Scaled dot-product attention and multi-head attention

  • Positional encoding and how Transformers handle sequence order

  • The structure of Transformer encoder and decoder architectures

  • Feedforward networks within Transformer layers

  • Layer normalization and residual connections used for stabilizing deep models

  • Training strategies and optimization methods for large Transformer models

  • Applications of Transformers in different areas of AI, such as natural language processing, computer vision, and multimodal learning

 

In addition, modern AI research has produced many important extensions and improvements of Transformers, including more efficient attention mechanisms, scaling techniques, and architectural variations.

 

Because Transformers sit at the center of many modern AI systems, studying them in depth requires time to understand both the theoretical foundations and practical implementations. For this reason, the topic is substantial enough to justify a full course dedicated to Transformers.

​

Is GPU needed for AI 310?

​​​

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

FAQs for AI 520

Why AI 410 is a prerequisite of AI 520?​​​​

​​​

AI 410 provides the fundamental background in deep learning and computer vision that is necessary for studying the advanced topics covered in AI 520.

​

In AI 410, students learn the core foundations of deep neural networks, including:

  • Multilayer Perceptron (MLP) models and basic neural network architectures

  • Training deep neural networks, including optimization and backpropagation

  • Convolutional Neural Networks (CNNs), which are the fundamental models used in computer vision

  • Canonical CNN architectures such as VGG and ResNet

 

These topics establish the key concepts required to understand more advanced computer vision and generative AI models.

 

In AI 520, the course moves to more advanced computer vision and generative AI tasks, including:

  • Object detection and advanced vision tasks

  • Modern generative AI methods for image generation

 

These topics rely heavily on the deep learning and CNN foundations introduced in AI 410. For example:

  • Many computer vision systems for tasks such as object detection are built on top of CNN-based architectures.

  • Many generative models for images rely on deep neural networks to learn visual representations.

 

Because AI 520 assumes that students already understand deep neural networks and convolutional neural networks, AI 410 is required as a prerequisite.

​

In AI 410, students learn the fundamental concepts that modern deep learning models are built upon, including:

  • Multilayer Perceptrons (MLP) and neural network architectures

  • Backpropagation and gradient-based optimization

  • Training dynamics of deep neural networks

  • Techniques such as Batch Normalization, and other stabilization methods

 

These concepts are essential because Transformer models are fundamentally deep neural networks composed of multiple layers of neural modules. For example:

  • Transformer architectures include feedforward neural networks (MLPs) inside each layer.

  • Training large neural networks requires understanding normalization techniques, which are critical for stabilizing Transformer training.

  • Many design choices in Transformers rely on the same optimization principles and training strategies used in deep learning.

 

Therefore, AI 410 provides the theoretical and practical foundation needed to understand how Transformer models work and how they are trained.

 

For this reason, AI 410 is required before taking AI 500.

​

Why is AI 500 a prerequisite for AI 520?

 

AI 500 introduces Transformer models, which have become one of the most important architectures in modern AI. Because many advanced methods in computer vision and generative AI are now based on Transformers, students need this background before taking AI 520.

 

In AI 500, students learn the key principles of Transformer architectures, including:

  • Attention mechanisms and self-attention

  • Transformer encoder and decoder structures

  • Training and scaling Transformer models

  • Applications of Transformers in modern AI systems

 

These concepts are essential for understanding many of the models studied in AI 520.

In computer vision, many recent methods are based on Transformer architectures, such as:

  • Vision Transformers (ViT) for image classification

  • Transformer-based models for object detection

  • Hybrid CNN–Transformer vision architectures

 

In generative AI, Transformers also play an important role. Many modern generative systems rely on Transformer-based models for generating and understanding multimodal data.

 

Because AI 520 covers advanced computer vision and generative AI models, it assumes that students already understand the Transformer architecture and attention mechanisms introduced in AI 500. For this reason, AI 500 is required as a prerequisite for AI 520.

​​​

Is GPU needed for AI 310?

​​​

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

Join our email list

Beaver-Edge AI Institute

E-mail: info@beaver-edge.ai

WeChat (微信): beaver-AI

© 2026 by Beaver-Edge AI Institute.

​

​

bottom of page