AI Basics

In this semester you'll dive headfirst into AI's exciting world. You'll master fundamental techniques like classification algorithms, bias detection, and model evaluation while seeing how they solve actual problems.

You explore concepts including training data balance, decision boundaries, and prediction accuracy—the building blocks of effective AI systems. You'll learn how to review model performance, detect overfitting, and
implement improvements through hyperparameter tuning.

The course covers practical applications: from clustering customer data and visualizing complex relationships to using sentiment analysis for content reliability across digital platforms. You'll discover how recommendation
systems work and explore the creative side of AI through generative models.

Through hands-on projects like analyzing satellite imagery, optimizing delivery routes, and predicting customer behavior, you'll gain experience with real-world scenarios. You’ll also examine how AI can enhance accessibility
and sustainability in urban environments.

By semester's end, you'll be equipped to translate client requests into AI solutions and make smart, responsible decisions in your future career. Let the adventure begin!

You’ll start with the Real-World Challenge—a group assignment where you collaborate with peers and a partner company to develop a practical AI solution for a real business problem. Alongside the group project, you’ll engage in several AI Explorations. These are short, hands-on experiments that allow you to delve into specific AI techniques and ethical issues. For example, in the “AI as Black Box” experiment, you’ll learn to use interpretability tools like SHAP and LIME to understand how AI models make decisions. Other experiments include projects like “Deepfake Detectives,” where you’ll learn to identify AI-generated content, and “AI Trainer,” which involves training and evaluating your own AI model using tools such as Google Colab and scikit-learn.

Additionally, you’ll undertake a Personal AI Quest, an individual project where you choose your own AI challenge to explore in depth. Throughout the semester, you’ll receive guidance from a teacher coach and a teacher
expert, with your progress continuously assessed through formative feedback sessions and a final portfolio evaluation.

After the Startsemester you can join AI Basics.

Suggested follow-up semesters in your 2nd year are AI, Machine Learning & Data, Applied Generative AI, Front End Development, Game Design, Media Creation and Mobile Apps Development. But depending on your personal interests and ambitions, any main semester could be feasible.