Machine-learning (ML) techniques are becoming more prevalent. ML techniques rely on mathematics and software engineering. Researchers and practitioners studying best practices strive to design ML systems and software that address software complexity and quality issues. Such design practices are often formalized as architecture and design patterns by encapsulating reusable solutions to common problems within given contexts. However, a systematic study to collect, classify, and discuss these software-engineering (SE) design patterns for ML techniques have yet to be reported. Our research collects good/bad SE design patterns for ML techniques to provide developers with a comprehensive classification of such patterns. Herein we report the preliminary results of a systematic-literature review (SLR) of good/bad design patterns for ML.