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Introduction

The vast amount of widely available data allows machines to solve challenging tasks without explicitly being programmed to do so, often outperforming existing methods based on domain knowledge and/or human experts.

In this course we focus on basic machine learning methods, both for supervised and unsupervised learning. We will look at how exactly machines "learn" from the data, and how they use the knowledge learned during training to solve tasks of interest.

The course covers both the theory (looking at the methods more rigorously than introductory machine learning courses) and the practice of machine learning (using Python).

Course content

• Recapitulation (supervised vs. unsupervised learning, linear regression and classification).
• Advanced regression and classification.
• Clustering.
• Neural networks (feedforward neural networks, backpropagation algorithm, stochastic gradient descent).
• Practical aspects of neural networks (e.g., architectures, weights initialization, normalization, regularization, dropout, optimized training methods).
• Ensemble methods (boosting and bagging).
• Model selection and assessment.