Chapter 0: Machine Learning Basics
This chapter introduces the basic concepts of Machine Learning. We therefore rely the excellent material from the I2ML Course which already comes with videos and has been taught LMU numerous times already.
The focus of these chapters in on introducing supervised learning, explaining the difference between regression and classification, showing how to evaluate and compare Machine Learning models and formalizing the concept of learning in general.
When taking our DL4NLP course, you do not necessarily have to re-watch all of the videos if you already have proficient knowledge in this area. Nevertheless, all the explained concepts represent the basis which be build our course upon and thus we expect every student to be familiar with the content.
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Chapter 0.1: ML Basics (I2ML)
This chapter introduces the basic concepts of Machine Learning. We focus on supervised learning, explain the difference between regression and classification, show how to evaluate and compare Machine Learning models and formalize the concept of learning.
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Chapter 0.2: Supervised Regression (I2ML)
This chapter treats the supervised regression task in more detail. We will see different loss functions for regression, how a linear regression model can be used from a Machine Learning perspective, and how to extend it with polynomials for greater flexibility.
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Chapter 0.3: Supervised Classification (I2ML)
This chapter treats the supervised classification task in more detail. We will see examples of binary and multiclass classification and the differences between discriminative and generative approaches. In particular, we will address logistic regression, discriminant analysis and naive Bayes classifiers.
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Chapter 0.4: Multiclass Classification (I2ML)
This chapter treats the multiclass case of classification. Tasks with more than two classes preclude the application of some techniques studied in the binary scenario and require an adaptation of loss functions.
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Chapter 0.5: Evaluation (I2ML)
This chapter treats the challenge of evaluating the performance of a model. We will introduce different performance measures for regression and classification tasks, explain the problem of overfitting as well as the difference between training and test error, and, lastly, present a variety of resampling techniques.