The classical problems were problems back in the day. There is no point in re-implementing them nowadays. But it is very important to know how they are implemented, and the logic behind them.
Because we are tackling much, much harder problems. Fields like data analysis, NLP, machine learning are yet to reach their potential. If we cannot master the easy classical problems, how are we expected to tackle the bigger fields? By master, I mean understand and not re-implement/re-invent the wheel every single time.