Must Learn
Published:
| Period | Time / wk | Advanced programming | Software dev | RL | Bayesian | Deliverable |
|---|---|---|---|---|---|---|
| Q1 (Sep–Nov 2025) | 11h (4/3/2/2) | Python internals (C-API, buffer protocol), NumPy/SciPy memory model; vectorization & numba; pick C++20+pybind11 or Rust+pyo3 for extensions; profiling basics (cProfile, perf) | Git strategies, pytest + coverage, mypy/ruff, packaging with poetry/pdm; CLI design; docs with Sphinx/MkDocs; CI via GitHub Actions & pre-commit | Refresher: MDPs/bandits; implement tabular DP/MC, ε-greedy; Gymnasium intro; clean-from-scratch code | Probability refresher; conjugate models; prior/posterior predictive checks; PyMC or Stan basics; ArviZ diagnostics | Tiny PyPI package (bio kernel or parser), benchmarks + doc site |
| Q2 (Dec 2025–Feb 2026) | 10h (4/2/2/2) | Native acceleration: C++/Rust ext modules; cache-aware data layouts; PyTorch custom ops shim; flamegraphs; microbenchmarks; intro GPU via Triton or CUDA kernels | Repro/data: DVC for datasets, dataset cards; semantic versioning; release wheels (Linux/Mac) | Policy Gradient (REINFORCE) from scratch; advantage baselines; experiment tracking (MLflow/W\&B) | Hierarchical models; GLMs; HMC/NUTS tuning; LOO/WAIC model comparison | Accelerated op (e.g., k-mer tally/UMI dedupe) as a PyTorch/JAX extension + reproducible DVC pipeline |
| Q3 (Mar–May 2026) | 11h (3/3/3/2) | GPU depth: memory coalescing, warp/wavefront basics; streams & async; JAX jit/pjit mental model | Workflow engines: Snakemake/Nextflow + Docker; config mgmt with Hydra; API sketch with FastAPI | Value-based RL: DQN (clean-room), target nets, replay buffers; sanity-check OOD & reward scaling | Gaussian Processes for time-series expression; sparse/inducing points; calibration | End-to-end pipeline (Nextflow) producing features → API serving a GPU-accelerated op; DQN repo with reproducible results |
| Q4 (Jun–Aug 2026) | 10h (3/2/3/2) | HPC touches: SIMD/AVX, OpenMP; SLURM; distributed training intro (FSDP/torch.distributed) | Observability: metrics/logging/tracing; perf budgets; simple K8s deploy or autoscaling container | PPO/A2C with robust training loops; eval protocol; basic safe/constrained tricks | Bayesian deconvolution for multi-omics; VI/ADVI; prior sensitivity & SBC | Year-capstone: open-source “fast-omics-kernels” + preprint-style tech report OR “RL-guided assay selection (sim)” with Bayesian uncertainty; public demo & docs |
learn advanced programming from: https://ramtung.ir/teaching.html Also, Software dev: https://ramtung.ir/teaching.html RL: https://deeprlcourse.github.io/ Bayesian: https://www.youtube.com/@rmcelreath