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Projektpraktikum II (Stochastik) (M26), Summer semester 2022

Location: Rudower Chaussee 25, Raum 2.207

Time: Tuesdays (Bi-weekly), 13:15pm – 14:45pm

Course catalog Agnes


The aim of the lab sessions provides students with the practical implementation of Machine Learning Algorithms. Python is used as the programming language. Main topics include; Getting started with Google Colab, preparing Data, Machine Learning Algorithms- Regression,
Classification and Reinforcement learning.

I recommend the class on   Mathematics for Machine and Reinforcement Learning and Markov Decision Processes (M27) by Prof. Dr. Dirk Becherer.


Basic Knowledge of Python.

Date Room Content Material Exercise
21.04  3.008 Getting started with colab, python installation, python libraries. Uploaded on moodle  
3.05 2.207 Methods of ML, Datasets,  Data loading for ML projects, Descriptive statistics, Data with Visualization. Uploaded on moodle Groups should work on 3 different dataset and perform descriptive and explanatory statistics.
17.05 2.207 Data pre-processing review, Classfying an ML Algorithm, ML Algorithms like Linear Regression, Logistic Regression, Decision Trees, Naive Bayes Algorithm etc with scikit-learn.   Groups should apply different ML algorithms to the 3 datasets with scikit-learn and write up a report for submision. Each group will present on the 31st May.
31.05 2.207      
14.06 2.207      
28.06 2.207      
12.07 2.207      


Links und Literature.