Learning Materials

Courses

Practical, dense AI and engineering courses. Beginner-friendly, no fluff.

Beginner

Intro to AI

Build real AI systems from first principles.

A ground-up course covering the math, code, and intuition behind modern AI, from neural networks to LLMs. Written and taught by Agastya Bhadani.

• Linear algebra & calculus for ML
• Building neural networks from scratch
• Training loops, loss functions, backprop
• Fine-tuning and prompting LLMs
• Building your first AI project

Advanced

Intro to Astrophysics

Model the universe in code: orbits, stars, black holes, cosmology.

A full-year, university-level computational astrophysics course. You write, validate, and defend numerical models of real physical systems, from two-body orbits to N-body clusters, exoplanet transits, compact objects, and the expanding universe. Anchored to MIT OCW, Open Yale, UC Berkeley, and NASA open data.

• Scientific Python, vectors & numerical error
• Gravity, orbits & symplectic integrators
• Calculus ramp: derivatives, integrals & ODEs
• Exoplanet transits, radial velocity & binaries
• Stars, white dwarfs, neutron stars & black holes
• N-body simulation, dark matter & cosmology
• A capstone simulation project + paper & defense