Machine Learning and Molecules (MLmol)
Course content
Students write their own neural network code from scratch using Python and use it to predict chemical properties of molecules. The performance is compared to standard machine learning packages such as scikit-learn, Keras, and DeepChem.
Knowledge:
Basic principles behind Python programming, machine learning, and
cheminformatics. Classification and regression using neural
networks. Activation functions, back propagation using
gradient descent, Overfitting, regularisation, hyperparameter
optimisation, and training/validation/test sets. SMILES
strings, molecular fingerprints, and graph convolution as applied
to molecules.
Skills:
Data manipulation and visualisation using Pandas, numpy, and
Matplotlib/Seaborn. Manipulation of chemical data using RDKit. Use
of scikit-learn, Keras, and DeepChem.
Competences:
Prediction of chemical properties using machine learning. Critical
evaluation of machine learning models.
Videolectures and classroom discussion
See Absalon for a list of course literature
First year organic chemistry and mathematics
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral examination, 30 minutes with no preparation time
- Type of assessment details
- A final 30-minute individual oral examination is without preparation and based on the project report. The exam begins with a 5 minute presentation from the examinee, after which the internal assessors ask questions in the project area.
- Exam registration requirements
-
All three assignments have to be approved by the end of the 7th week; b) the exam report has to be submitted by the end of the 8th week of the block
- Aid
- All aids allowed
The use of Large Language Models (LLM)/Large Multimodal Models (LMM) – such as ChatGPT and GPT-4 – is permitted.
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
Several internal examiners
- Re-exam
-
Same as ordinary exam.
The same report, possibly improved based on feedback, can be used for the oral re-exam.
Criteria for exam assessment
See Learning Outcome
- Category
- Hours
- Class Instruction
- 12
- Preparation
- 93,5
- E-Learning
- 50
- Project work
- 50
- Exam
- 0,5
- English
- 206,0
Kursusinformation
- Language
- English
- Course number
- NKEB22002U
- ECTS
- 7,5 ECTS
- Programme level
- Bachelor
- Duration
-
1 block
- Placement
- Block 2, Block 3 And Block 4
- Schedulegroup
-
Much of the instruction comes from video lectures supplemented by one weekly meeting for Q&A.
- Capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
- Studyboard
- Study Board of Physics, Chemistry and Nanoscience
Contracting department
- Department of Chemistry
Contracting faculty
- Faculty of Science
Course Coordinator
- Jan Halborg Jensen (8-75737570797e70794b6e737078397680396f76)
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Kursusinformation for indskrevne studerende