Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed.

We are living in an information era where the amount of image and video data increases exponentially.

Striga hermonthica, commonly known as witchweed, infests major cereal crops in Sub-Saharan Africa causing severe yield losses and threatening the livelihood of millions of resource poor farmers.

With the dramatic growth of the image data on the web, there is an increasing demand of the algorithms capable of understanding the visual information automatically.

In this thesis we explore machine and deep learning approaches that address keychallenges in high dimensional problem areas and also in improving accuracy in wellknown problems. In high dimensional contexts, we have focused on computational fluid dynamics (CFD) simulations.

In 2018, the number of mobile phone users will reach about 4.9 billion. Assuming an average of 5 photos taken per day using the built-in cameras would result in about 9 trillion photos annually.

We explored how to morphologically classify well-resolved jetted radio-loud active galactic nuclei (RLAGN) in the LOw Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) using machine learning.

Natural history collections provide invaluable sources for researchers with different disciplinary backgrounds, aspiring to study the geographical distribution of flora and fauna across the globe as well as other evolutionary processes.