Teaching

On this page you find information about lectures we are offering. In addition, we provide information about the preparation of MSc/BSc thesis and PhD thesis.  

Computational Diagnostics of Data - DATA.ML.390 

The lecture Computational Diagnostics of Data is a 5 cr course offered at Tampere University (MSc level). 

After completing the course, the student gained a basic understanding of the definition and the meaning of computational diagnostics and its utility for biomedical research. Case studies will be discussed illustrating the interplay between computational and statistical methods that are applied to large-scale and high-dimensional data sets from various experiments. Moreover, the student will learn how to practically approach such problems by using the statistical programming language R. In general, the course teaches statistical thinking in the context of biomedical problems, i.e., the adaptation of machine learning methods in a problem specific manner.      

The name of the course emphasizes that adopting a mechanistic or cookbook-type approach to data science is destined to be unsuccessful, underscoring the necessity for active involvement and critical thinking from the analyst. The overall goal of the course is to learn statistical thinking.

• DATA.ML.390: Computational Diagnostics of Data

The textbook of the course is: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R

Introduction to Probability and Statistical Inference MATH-APP.210 

The lecture Introduction to Probability and Statistical Inference is a 5 cr course offered at Tampere University (BSc level, English implementation). 

After completing the course, the student gained a basic understanding of the definition and the meaning of computational diagnostics and its utility for biomedical research. Case studies will be discussed illustrating the interplay between computational and statistical methods that are applied to large-scale and high-dimensional data sets from various experiments. Moreover, the student will learn how to practically approach such problems by using the statistical programming language R. In general, the course teaches statistical thinking in the context of biomedical problems, i.e., the adaptation of machine learning methods in a problem specific manner.      

• MATH-APP.210: Introduction to Probability and Statistical Inference  

Textbook of the course: In preparation.

Master thesis

We provide interesting projects for students to perpare a Master (MSc) thesis (or BSc thesis). Below we list some publications that resulted from MSc  projects.

• Smolander, J., Dehmer, M., & Emmert-Streib, F. (2019). Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders. FEBS Open Bio, 9(7), 1232-1248.
• Moore, D., de Matos Simoes, R., Dehmer, M., & Emmert-Streib, F. (2019). Prostate Cancer Gene Regulatory Network Inferred from RNA-Seq Data. Current genomics, 20(1), 38-48. 
• Smolander, J., Stupnikov, A., Glazko, G., Dehmer, M., & Emmert-Streib, F. (2019). Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients. BMC cancer, 19(1), 1176. 
• Yang, Z., Dehmer, M., Yli-Harja, O., & Emmert-Streib, F. (2020). Combining deep learning with token selection for patient phenotyping from electronic health records. Scientific reports, 10(1), 1432.

Contact us when you are interested in such a project.

PhD thesis

We are also providing interesting projects for gifted students to prepare a PhD thesis. If interested please send the following information:  

• CV (including information about GPA)
• Brief research statement (1 page)
• What fellowship will you apply

Contact us when you are interested in such a project.