1. Machine learning project excersie
2016/2017

2. Task
End-to-end solution of an arbitrary supervised machine learning task
2 students = 1 team
The 2 team members has got dedicated tasks and can work independently from each other
„registering” as a team: until 13/03/2017 in
otherwise random teams
Tasks:
Task selection and planning
Data preprocessing, feature extraction
Modell comparison, evaluation
Presentation (8-10min / team) on the last week of the semester
Max 25 scores / Min 12.5 scores
Implementation
Any programming language, any machine learning tool
Results, discussions and reasons have to be understood!

3. Timeline 13/03/2017 team registration (e-mail)
20/03/2017 task selection ( ) – link
03/04/2017 project plan ( ) – 2 pages
14/05/2017 deliverables ( ) – codes/scripts, data, results
16?/05/2017 presentation

4. Assessment Overall: 12.5/25 scores Complexity of the task Motivation
Preprocessing
Data records vs. Text/image/sound
Noise reduction
Baseline methods
Team-level score (based on the plan)

5. Assessment Feature extraction (1. team member)
Only precalculated features: 0
Simple own features: 2.5
Novel features and/or feature combinations: +2.5
Machine learning approaches and meta-parameter tuning (2. team member)
Wrong model: 0p
One single model: 0p
Comparision of various models: +2.5p
Extensive parameter tuning of models: +2.5p

6. Assessment Extra techniques (both member)
Improvement of the results!
For example: feature selection, dimension reduction, exploiting unlabeled data, deep learning, etc
Analysis of results (both member)
Meaningful baselines: 2.5
Analysis with various evaluation metrics: +2.5
Error analysis: +2.5
Presentation (both member)
Followable results 2.5
Discussion/explanation of model choice/findings +2.5

7. Recommended(!) projects
You can find a task/dataset!
Kaggle.com
Playground
Getting started
In Class
UCI
Other examples
Text classification
Image classification
Regression

8. Recommended(!) tools Java Python R/Matlab/Octave Weka Mallet
Scikit-learn
Orange
R/Matlab/Octave