2025 Spring, Molecular Modeling and AI Chemistry

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Course Overview

Course Code
CH6001550
Course Description
The course aims to equip graduate students with theoretical knowledge of machine learning techniques applicable to research and to provide hands-on experience in implementing machine learning models.
Time and Classroom
Wed 1:30–4:30 pm, Room 409
Evaluation
paper presentation (50%), project presentation (50%)

Schedule

Week Date Topic
1 3/5 Course overview / Perceptron
2 3/12 Multilayer perceptron
3 3/19 Deep neural network / Convolutional neural network (CNN)
4 3/26 Residual neural network (ResNet) / Recurrent neural network (RNN)
5 4/2 Autoencoder / Encoder-decoder
6 4/9 Transformer / Large language models (LLMs)
7 4/16 Generative models
8 4/23 No class (KCS meeting)
9 4/30 Paper presentations
10 5/7 Project planning
11 5/14 Cheminformatics 101
12 5/21 Deep learning for chemical property analysis
13 5/28 Deep learning optimization
14 6/4 Project presentations (1)
15 6/11 Project presentations (2)
16 6/18 No class (final)