AI education built from the ground up — technically rigorous, ethically grounded, pedagogically sound.
I built a three-track AI curriculum from scratch — covering supervised and unsupervised learning, CNNs, NLP, and reinforcement learning, implemented in Python on real-world datasets, with ethics woven into every module. Students from that program are now at Yale and UPenn conducting original AI research.
Good AI curriculum does not separate the technical from the ethical. It teaches both together, in context, with honesty about what is known and what remains uncertain. That is what I design.
The AI Principles program I designed consists of three interlocking tracks, each building on the previous and each integrating ethics not as a separate module but as a continuous thread running through every technical concept.
Introduction to supervised learning: linear regression, classification, K-Nearest Neighbors, decision trees. Introduction to unsupervised learning: clustering, dimensionality reduction. Linear algebra for machine learning: vectors, matrices, transformations. All implemented in Python on real-world datasets. Ethics thread: what does it mean for a model to be fair? Whose data? Whose labels?
Neural networks from first principles: perceptrons, backpropagation, gradient descent. Convolutional neural networks for image recognition. Natural Language Processing: tokenization, embeddings, transformers. Reinforcement learning: agents, environments, reward functions. Ethics thread: what do models lose when trained on recursively generated data? Who decides what the model rewards?
Computer Science Essentials: algorithms, data structures, computational thinking. Python programming from first principles through advanced applications. Mathematics through Calculus 3: the quantitative foundation for machine learning. This track serves both as prerequisite scaffolding and as a standalone course for students building technical fluency.
Ethics is not a separate module in my curriculum. It is a continuous thread. Every technical concept is paired with an ethical question: What does it mean to train on this data? Who is harmed when this model fails? What does this loss function actually optimize for? Students graduate knowing that understanding AI requires asking both how and why.
I design AI and data science curricula for schools, universities, corporate training programs, and nonprofit initiatives. Every curriculum is built around your learners — their background, their goals, and what they actually need to be able to do.
Age-appropriate AI education for middle and high school students. Builds technical competency alongside critical thinking about AI's role in society. Designed to prepare students for university-level work in the field.
Curriculum design for undergraduate and graduate AI and data science courses. Emphasis on mathematical foundations, practical implementation, and ethical grounding — designed to produce graduates who understand what they are building and why it matters.
Structured multi-session learning programs for organizations building internal AI literacy. Designed for employees at all levels, from entry-level to senior leadership, with differentiated content and assessments.
Review and improvement of existing AI or data science curricula. I evaluate technical accuracy, ethical integration, pedagogical effectiveness, and alignment with learning objectives. Delivered as a written report with actionable recommendations.
A good AI curriculum does not just transfer information. It builds the capacity to think clearly about complex, fast-moving systems. It teaches students to ask the right questions, to hold uncertainty without paralysis, and to understand that every technical decision is also a value decision.
Students who graduated from the curriculum I built at North Shore Hebrew Academy are doing original AI research at Yale and UPenn. That outcome is not an accident. It is the result of treating students as thinkers from day one.
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