Sure, I can try to explain the difference between soft computing and machine learning. According to 1, soft computing is one mathematical domain that employs the concepts of probabilistic reasoning and evolutionary computation to deal with problems that are hard to model or solve precisely. 

Machine learning is the discipline that attempts to improve a machine’s performance of a task, given examples. Machine learning could be considered a part of artificial intelligence, which is the intellectual project of trying to capture all aspects of human intelligence in computers. Soft computing involves processes that involve indirect, approximate solutions instead of binary algorithms, widely considered to include such technologies as fuzzy logic, neural networks, and genetic algorithms. 

There is a broad overlap between these techniques and a certain planning and learning subset of AI, control theory, complex systems theory, etc. Machine learning techniques are mostly based on computational statistics. I hope this helps.
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