Chemistry 142/242 (cross-listed with BioE): Machine Learning, Statistical Models, and Optimization for Molecular Problems
Instructor: Prof. Teresa Head-Gordon
An introduction and current advances in machine learning for the physical sciences. Machine learning prerequisites are introduced including local and global optimization, various statistical and clustering models, and early meta-heuristic methods such as genetic algorithms and artificial neural networks. Building on this foundation, recent machine learning techniques are covered including: deep learning artificial neural networks, convolutional neural networks, recurrent and long short term memory (LSTM) networks, graph neural networks, generative networks, decision trees, Seq2Seq. Various case studies in applying optimization, statistical modeling, and machine learning methods as classification and regression tasks in different areas of (bio)chemistry including enzymology, synthesis, physical property prediction, and automated energy and force generation for molecular dynamics will be covered, including AlphaFold2. A finals project defined by research interest for graduate students and/or projects for undergraduates will be offered.
Course materials available on bspace.berkeley.edu for enrolled students and guests.
Taught: Fall 2019; Spring 2020, 2021, 2022, 2023, 2024
Chemistry 277B: Machine Learning ALGORITHMs
| Course Description: An introduction to mathematical optimization and statistics and “non-algorithmic” computation using machine learning. Machine learning prerequisites are introduced including local and global optimization, various statistical and clustering models, and early meta-heuristic methods such as genetic algorithms and artificial neural networks. Building on this foundation, current machine learning techniques are covered including Deep Learning networks, Convolutional neural networks, Recurrent and long short term memory (LSTM) networks, Generative models and Graph Neural Networks. Various case studies in applying machine learning methods as classification and regression tasks in different areas of chemistry including synthesis, physical property prediction, and automated energy and force generation for molecular dynamics will be covered. Impacts of ML including Seq2Seq and AlphaFold-2 will be covered. A group finals project defined by research interest will be offered.
Course materials available to MSSE students only Taught: Spring 2021, 2022, 2023, 2024 |
Bioengineering 100: Ethics in Science and Engineering
Instructor: Prof. Teresa Head-Gordon
The goal of this semester course is to present the issues of professional conduct in the practice of engineering, research, publication, public and private disclosures, and in managing professional and financial conflicts. The method is through historical didactic presentations, case studies, presentations of methods for problem solving in ethical matters, and classroom debates on contemporary bioethical issues.
Course materials available on bspace.berkeley.edu for enrolled students and guests.
Taught: Fall 2008, 2012, 2013, 2014, 2023; Spring 2010, 2011, 2012, 2021
BioE 103: Engineering Molecules 2
Instructor: Prof. Teresa Head-Gordon
Thermodynamic and kinetic concepts applied to understanding the chemistry and structure of biomolecules and their features. Topics include entropy, bioenergetics, free energy, chemical potential, reaction kinetics, enzyme kinetics, diffusion and transport, non-equilibrium systems, and their connections to the cellular environment.
Taught: Fall 2016, Fall 2018, Fall 2019, Fall 2022
Chemistry 1A: General Chemistry
Instructor: Prof. Teresa Head-Gordon
Stoichiometry of chemical chemical reactions, quantum mechanical description of atoms, the elements and periodic table, chemical bonding, real and ideal gases, thermochemistry, introduction to thermodynamics and equilibrium, acid-base and solubility equilibria, introduction to oxidation-reduction reactions, introduction to chemical kinetics.
Taught: Fall 2015, Fall 2016, Fall 2020
Chemistry C130: Biophysical Chemistry: Physical Principles and the Molecules of Life
Instructor: Prof. Teresa Head-Gordon, Prof. Jamie Cate, Prof. Bryan Krantz
Thermodynamic and kinetic concepts applied to understanding the chemistry and structure of biomolecules (proteins, DNA, and RNA). Molecular distributions, reaction kinetics, enzyme kinetics. Bioenergetics, energy transduction, and motor proteins. Electrochemical potential, membranes, and ion channels.
Course materials available on bspace.berkeley.edu for enrolled students and guests.
Taught: Spring 2013, 2014, 2015
Bioengineering 143/243: Computational Methods in Biology
Instructor: Prof. Teresa Head-Gordon
An introduction to biophysical simulation methods and algorithms, including molecular dynamics, Monte Carlo, mathematical optimization, and “non-algorithmic” computation such as neural networks. Various case studies in applying these areas in the areas of protein folding, protein structure prediction, drug docking, and enzymatics will be covered.
Course materials available on bspace.berkeley.edu for enrolled students and guests.
Taught: Fall 2009, 2010, 2011; Spring 2008, 2009 (co-taught with Prof. Berend Smit)