BMB 961 Sec 003 Semester Schedule
BMB 961 Sec 003 Semester Schedule¶
Day |
Topic |
|---|---|
Lec 1 (Jan 11) |
Class introduction, review materials |
Lab 1 (Jan 13) |
Introduction to Google Colab, python review |
Lec 2 (Jan 18) |
Basics of Machine Learning |
Lab 2 (Jan 20) |
First steps with a neural network |
Lec 3 (Jan 25) |
Advanced neural networks |
Lab 3 (Jan 27) |
Learning something useful |
Lec 4 (Feb 1) |
Generating things with ML |
Lab 4 (Feb 3) |
GAN lab |
Lec 5 (Feb 8) |
Protein structure prediction |
Lab 5 (Feb 10) |
Protein structure prediction |
Lec 6 (Feb 15) |
Hamiltonian Mechanics |
Lab 6 (Feb 17) |
Liquid Argon simulation Day #1 |
Lec 7 (Feb 22) |
Periodic boundaries and thermostats |
Lab 7 (Feb 24) |
Liquid Argon simulation Day #2 |
Lec 8 (Mar 1) |
Biomolecular simulations |
Lab 8 (Mar 3) |
Running simulations with OpenMM |
Mar 7 - 11 |
Spring Break |
Lec 9 (Mar 15) |
Simulation analysis: clustering and Markov state models |
Lab 9 (Mar 17) |
Trajectory featurization, clustering and network analysis |
Lec 10 (Mar 22) |
Parameterization of molecular dynamics forcefields with ML |
Lab 10 (Mar 24) |
ML-based forcefield laboratory |
Lec 11 (Mar 29) |
Reactive forcefields with neural networks |
Lab 11 (Mar 31) |
ANI simulations |
Lec 12 (Apr 5) |
Analysis of MD simulations using neural networks |
Lab 12 (Apr 7) |
Learning collective variables with VAMPnets |
Apr 12 - Apr 26 |
Independent research projects |
Last class period (Apr 26) |
Presentations for independent projects |
This is the tentative schedule prior to the start of the semester. The overall content shouldn’t change, but ordering or deadlines may shift somewhat. If deadlines change, substantial notice will be given!