# Lecture: Data Mining and Machine Learning in Bioinformatics (WS 2018/19)

**Location: **B-IT, room 0.107

**Time: Mo, 9:30 - 11:00**

**Exercises: NONE **

**Prerequisites: **Bachelor Computer Science or equivalent qualification

**Content:** This lecture gives a broad overview about data science methods, which are frequently used in life science informatics. The goal is to understand the explained methods and being able to apply them correctly in the life science context. More detailed, the the following topics are covered:

- Statistical Basics of Data Mining
- Descriptive statistics and statistical plots
- Probability distributions
- Statistical hypothesis testing
- Linear models, logistic regression
- Principal Component Analysis

- Cluster Analysis
- Basic methods: hierarchical clustering, k-means
- Gaussian Mixture Models, consensus clustering, Non-negative matrix factorization

- Classical Supervised Machine Learning Methods
- Principles of supervised learning: bias-variance trade-off, regularized risk minimization
- Penalized generalized linear models: lasso, ridge, elastic net
- Random Forests
- optionally: Gradient Boosting

- Deep Learning Approaches
- (Convolutional) Deep Neural Networks, Recurrent Neural Networks
- (Variational) Autoencoders, Generative Adverserial Networks

- Optionally: Bayesian Networks

Prerequisites for getting credits are:

- passing of exam

# Seminar: Machine Learning Methods in the Life Sciences (SS 2019)

**Location:** 3.110, b-it

**Time: **Mo, 9:30 - 11:00am

**Prerequisites:**

- B.sc. Computer Science or equivalent qualification
- Knowledge about basic supervised and unsupervised machine learning techniques, e.g. neural networks, Random Forests, SVMs, PCA

**Credits**:

- presentation of one chosen topic
- regular attendance