6 EC
Semester 2, period 5
5254MLIC6Y
| Owner | Master Chemistry (joint degree) |
| Coordinator | dr. ir. B. Ensing |
| Part of | Master Chemistry (joint degree), track Molecular Sciences, |
| Links | Visible Learning Trajectories |
The course Machine Learning for Chemistry will provide a broad understanding of current deep learning methodologies and their application in chemical research. Rather than a formal exposure, it will consist of a more hands-on approach tailored to students interested in applying deep learning to (molecular) scientific problems. The course is targeted at a broad audience: from theoretical chemists who wish to dive into data-driven science, to experimental chemists keen on integrating machine learning in their work.
The course will first review briefly foundational aspects of probability and information theoretic concepts together with an overview of machine learning basics (as treated more detailed in the chemistry bachelor course AI in Chemistry). We will then focus on a range of popular deep learning techniques that are particularly useful in chemistry, including graph-neural networks, energy-based models, diffusion models, large language models, and reinforcement learning. The exposition of deep learning models will be illustrated on relevant chemical applications, such as structure-property prediction and the generation of molecules with specific properties.
The course is provided as a lecture series (2 times 2 hours per week) plus hands-on laptop sessions (1 time 2 hours per week). The theoretical aspects of deep learning and generative AI for molecular science, taught in the lectures, will be applied by programming assignments during the laptop sessions. The laptop assignments start in the first weeks with deep learning exercises provided as Jupyter Notebooks that run in an internet browser on the laptop and contain information, open questions and (to be completed) Python computer codes. In the second part, the students will work in pairs on one larger deep learning project, during which they develop and implement a deep learning algorithm for a molecular science application. The final Jupyter notebook together with a presentation of the project in the last week will count for 35% of the final grade. The other 65% of the grade is obtained with a written exam.
Proficiency with programming in Python is helpful, if not prerequisite.
Having passed the Chemistry Bachelor course "AI for Chemistry" is also advantageous, but not essential.
Book: "Deep Generative Modeling" by Jakub M. Tomczak
Python, Jupyter notebooks, Google Colab
Relevant libraries: Numpy, scikit-learn, PyTorch, DeepChem
Powerpoint slides
|
Activity |
Hours |
|
|
Hoorcollege |
28 |
|
|
Laptopcollege |
14 |
|
|
Tentamen |
2 |
|
|
Werkcollege |
0 |
|
|
Self study |
124 |
|
|
Total |
168 |
(6 EC x 28 uur) |
This programme does not have requirements concerning attendance (TER part B).
| Item and weight | Details |
|
Final grade | |
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0.35 (35%) Laptop project assignment | Must be ≥ 5.5 |
|
0.65 (65%) Tentamen | Must be ≥ 5.5 |
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
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| 8 |
A modern laptop is needed for the computer practica. At least one week in advance of the first lecture, information on preparing the laptop will be made available on the Canvas website. In particular, installation of (mini-)conda/mamba with an environment of python packages is required to take part of the computer practica. Windows users need to setup/install WSL with a version of Linux, such as Ubuntu, as explained on the Canvas site.