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Lab 10 - Machine Learning
Objectives
Understand basic concepts of machine learning
Remember examples of real-world problems that can be solved with machine learning
Learn the most common performance evaluation metrics for machine learning models
Analyse the behaviour of typical machine learning algorithms using the most popular techniques
Be able to compare multiple machine learning models
Exercises
The exercises will be solved in Python, using various popular libraries that are usually integrated in machine learning projects:
Scikit-Learn: fast model development, performance metrics, pipelines, dataset splitting
Pandas: data frames, csv parser, data analysis
NumPy: scientific computation
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All tasks are tutorial based and every exercise will be associated with at least one “TODO” within the code. Those tasks can be found in the exercises package, but our recommendation is to follow the entire skeleton code for a better understanding of the concepts presented in this laboratory class. Each functionality is properly documented and for some exercises, there are also hints placed in the code.
Because the various tasks and exercises are spread throughout the laboratory text, they are marked with a ⚠️ emoji. Make sure you look for this emoji so that you don't miss any of them!
⚠️ [10p] Exercise 6
⚠️⚠️ NON-DEMO TASK
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References