n.BA.XX.MDMML.23HS (Machine Learning) 
Module: Machine Learning
This information was generated on: 15 May 2024
No.
n.BA.XX.MDMML.23HS
Title
Machine Learning
Credits
2

Description

Version: 2.0 start 01 August 2023

 

Study Programme Biotechnology / Chemistry
Regulations Applicable RPO, 29 January 2008, School of Life Sciences and Facility Management Academic Regulations, 15 Dec. 2009, Annex for the Bachelor of Biotechnology degree programme

RPO, 29 January 2008, School of Life Sciences and Facility Management Academic Regulations, 15 Dec. 2009, Annex for the Bachelor of Chemistry degree programme
Module Type  
X Compulsory Module    Elective Module    Optional Module
Planned Semester 5th Semester
Module Coordinator Prof. Dr. Jürgen Stohner
Telephone / E-Mail +41 (0)58 934 54 93 / juergen.stohner@zhaw.ch
Lecturer(s),
Speaker(s),
Associate(s)		 Prof. Dr. Jürgen Stohner, Guest lecturer
Entrance Requirements IT Skills within the Bachelor's Degree Programme
Learning Outcomes and Competencies Digital, computer-assisted methods are at the forefront of the life sciences. Using informatics, models for chemical or biotechnological questions are continually being developed and numerically processed. To extract scientific insights from data collection, a creative approach to handling vast amounts of data using algorithms with consideration for statistical methods is necessary (‘Machine Learning’). By the end of the module, students will be able to:

Understand, apply and critically assess computer algorithms based on statistical methods (neural networks, artificial intelligence, data science, data mining, etc.) using Machine Learning.
Module Content In the first half of the module, the fundamentals of machine learning and artificial intelligence (AI) based on Python (PyTorch, NumPy, SciPy, etc.) are introduced. In the second half, different experts will give weekly presentations on selected topics from current practice in the fields of chemistry and biotechnology. 
Follow-up Modules -
Methods of Instruction  Face-to-face and online
Digital Resources Relevant resources will be announced before the start of the course.
Lesson Structure / Workload  
 Contact Hours 30
 Guided Self-Study 15
 Independent Self-Study 15
 Total Workload 60
Classroom Attendance Yes
Assessment
  • Written exam at the end of the semester  50%
  • Written assignment 50%
Language of Instruction  German/English
Comments -

 

Course: Machine Learning
No.
n.BA.XX.MDMML.23HS.V
Title
Machine Learning

Note

  • No module description is available in the system for the cut-off date of 15 May 2024.