Around 18 months ago, I lost my job due to the COVID-19 pandemic. I was working as a part-time tutor while in college. The money I got from tutoring was used to cover expenses like food, petrol, and my car. After the government imposed lockdown restrictions on the entire country, I was unable to continue teaching. I couldn't go to college either and had to study at home.

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Detecting anomalies is critical in conducting surveillance, countering credit-card fraud, protecting against network hacking, combating insurance fraud, and many more applications in government, business and healthcare. Sometimes, the analyst has a set of known anomalies, and identifying similar anomalies in the future can be handled as a supervised learning task (a classification model). More often, though, little or no such "training" data are available. In such cases, the goal is to identify cases that are very different from the norm. Some techniques (clustering, nearest neighbors) may be familiar to you, others less so (e.g. based on information theory or spectral techniques).