This implementation-oriented course offers hands-on experience with current algorithms and approaches in Machine Learning and Artificial Intelligence, and their application to real-world learning and decision-making tasks. Praktikum will also cover empirical methods for comparing learning algorithms, for understanding and explaining their differences, for analyzing the conditions in which a method is more suitable than others.
On weekly or bi-weekly basis, we shall implement linear models for predictions (Linear Regression, Logistic Regression), classification trees (Decision trees), prototype method for clustering (K-Means), prototype classification methods (K-Nearest Neighbor, Naive Bayes classifier, Support Vector Machines) and link-based ranking algorithm PageRank.
Organization:- Tutorials will be held every week
- An implementation assignment will be given every week
- A solution to the assignments is discussed in the next lab session
- The final grade of praktikum depends on the points in each submitted assigment
Start: We start off on Thursday, 20.10.2016, with the general introduction to tutorial and implementation tools.
Textbooks:
- Kevin P. Murphy (2012): Machine Learning, A Probabilistic Approach, MIT Press.
- Wes McKinney (2012): Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython, O’Reilly
- Joel Grus (2015): Data Science from Scratch First Principles with Python, O’Reilly
- Willi Richert, Luis Pedro Coelho (2013): Building Machine Learning Systems with Python, PACKT
Registration:
- You can register for Praktikum through LSF
- For further details please contact Mohsan Jameel
