Home » project, Summer 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.
|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.|