In a classical Machine Learning scenario, data (e.g., traffic signs) is fed in random order into the model (e.g., traffic sign predictor) for every training iteration. While the model would eventually learn the difference between each data class (e.g., traffic sign type), randomly feeding the data into the model might slow the learning process: one would think it would be faster to differentiate between the very distinct data classes (e.g., the Stop sign has a unique shape) first (i.e., early training rounds) and latter (i.e., later training rounds) focus on the more similar classes (e.g., the Speed Limit 100 and 120). 
 
On the other hand, the model does not know which classes are similar or distinct before training. In fact, this is what it aims to learn in the first place. The student's task is to review the previous research efforts toward solving this chicken-egg problem and to propose and implement a novel speed-up solution.  

For similar topics, please visit https://crysys.hu/member/pejo#projects