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.

 

Learning outcome

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

Written
Oral
Collective
Continuous feedback during the course
Feedback by final exam (In addition to the grade)
ECTS
7,5 ECTS
Type of assessment
Oral examination, 30 min
Type of assessment details
In the event of timely submission, a final 30-minute individual oral examination is held during the block's examination period without preparation based on the project report. The exam begins with a 10-20 minute presentation from the examinee, after which the internal assessors ask questions in the project area.
Aid
All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Several internal examiners
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 1, Block 2, Block 3, Block 4 And Summer
Schedulegroup
Much of the instruction comes from video lectures supplemented by one weekly meeting for Q&A.
Capacity
The number of seats may be reduced in the late registration period
Studyboard
Study Board of Physics, Chemistry and Nanoscience
Contracting department
  • Department of Chemistry
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Jan Halborg Jensen   (8-6d6b6d687176687143666b6870316e7831676e)
Saved on the 28-02-2022

Are you BA- or KA-student?

Are you bachelor- or kandidat-student, then find the course in the course catalog for students:

Courseinformation of students