Machine Learning (including SciKitLearn) (32 hours)

Teacher: Simone Scardapane (Sapienza University of Rome)

The course will be structured in two parts:
Part I – Foundations. This part will give a broad introduction to machine learning, data mining, and statistical pattern recognition. The topics will include supervised learning (e.g., support vector machines, kernels, neural networks) and unsupervised learning (e.g., clustering, dimensionality reduction, deep learning). The issue of dimensionality of data will be also discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described.

Part II – Applications. This part will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. Starting from numerous case studies and applications, the student will learn how to apply machine learning algorithms to computer vision, smart robotics, text understanding (web search, anti-spam), medical informatics, database mining, and other areas. The learners will be also able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting).

Suggested reading
Géron, A. (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. ” O’Reilly Media, Inc.”.