Hiveverse Bootcamp

Week 1 — ML Fundamentals & Intuition

• Module 1.1: The ML Mindset – problem‑first framing, data exploration.
• Module 1.2: Linear & Logistic Regression – Titanic challenge, AUC‑ROC, error trade‑offs.
• Module 1.3: ML Landscape & Taxonomy – supervised vs unsupervised, algorithm personalities.
• Module 1.4: Optimization & Learning Theory – gradient descent game, No Free Lunch intro.

Week 2 — Practical ML & Initial Project

• Module 2.1: Feature Engineering & Data Prep – competition & "feature detective".
• Module 2.2: Model Evaluation & Validation – cross‑validation, model court simulation.
• Module A: Kolmogorov Corner – algorithmic information theory & compression challenge.
• Module 2.3: ML Ethics & Real Stakes – demilitarization, "hard choices" simulation.
• Capstone: Build‑a‑Baseline — churn, sentiment transfer, anomaly detection.

Weeks 1‑2 — Neural Network Fundamentals

NumPy NN from scratch, backpropagation, optimization olympics.

Weeks 3‑4 — Specialized Architectures

CNNs, RNNs, modern training techniques with visualizers.

Weeks 5‑6 — RL & Efficient Fine‑tuning

RL foundations, LoRA & adapter‑based tuning.

Weeks 7‑8 — Transformers & Attention

Self‑attention, transformer build‑up, pre‑trained model adaptation.

Weeks 9‑10 — Capstone & Advanced Topics

Self‑supervised, generative, multi‑modal learning & architecture defense.

Weeks 1‑2 — Distributed Training Foundations

Data/model/pipeline parallelism, gradient traffic control.

Weeks 3‑4 — Communication‑Efficient Training

DCT & sparsification, EATreeSGD synchronization.

Weeks 5‑6 — Parallel Evolution & Auto‑Tuning

Seed chase, evolutionary strategies at scale.

Weeks 7‑8 — Decentralized Ecosystem

Federated learning, fault tolerance drills.

Weeks 9‑10 — AutoML & Self‑Improving Systems

NAS, continuous updating AI colony project.