Transitioning from a mechanical engineering background to a career in machine learning and artificial intelligence (ML/AI) is certainly possible. Here's how you can make a strategic move by pursuing a graduate degree:

**1. Target a Relevant Graduate Degree:**

  - **Master's in Computer Science (MCS) or Master's in Artificial Intelligence**: These programs are highly relevant and provide a strong foundation in the theory and practice of AI and ML.

**2. Research Your Prospective Programs:**

  - Look for programs that offer courses or specializations in AI, ML, and Robotics.

  - Consider schools known for their research and faculty expertise in these areas.

**3. Skill Development:**

  - While pursuing your degree, gain hands-on experience in AI/ML. This can include internships, personal projects, and collaborations with professors.

  - Learn programming languages commonly used in AI/ML like Python, and dive into libraries such as TensorFlow and PyTorch.

  - Develop a strong understanding of linear algebra, calculus, statistics, and probability, as these are fundamental to ML/AI.

**4. Robotics vs. AI/ML:** 

  - An MS in Robotics can be a good choice if you're interested in combining AI/ML with robotics, as these fields often intersect. You can focus on AI algorithms for robotics and automation.

  - If your goal is broader and you want to work on AI/ML in various domains beyond just robotics, a more general AI/ML degree might be better.

**5. Work on Projects:**

  - Showcase your practical skills by working on real-world projects. Start with small projects and gradually move on to more complex ones.

  - Participate in AI/ML competitions or contribute to open-source projects.

**6. Networking:**

  - Attend conferences, seminars, and meetups related to AI/ML. Networking can help you connect with professionals in the field and learn about job opportunities.

**7. Build a Portfolio:**

  - Create a portfolio that demonstrates your AI/ML skills. This could include a GitHub repository showcasing your projects, a personal website, or a blog where you explain AI/ML concepts.

**8. Internships:**

  - Secure internships in AI/ML roles, even if they are short-term. Practical experience is invaluable when transitioning careers.

**9. Apply for AI/ML Roles:**

  - Once you've gained knowledge and experience, apply for positions related to AI/ML engineering, data science, or research, depending on your interests and skills.

**10. Lifelong Learning:**

  - AI/ML is a rapidly evolving field. Be prepared to keep learning and stay updated on the latest trends and technologies.

Remember that your background in mechanical engineering can be an asset, especially in applications like robotics and automation, where AI/ML is increasingly vital. Your unique perspective and skills can set you apart in this interdisciplinary field. With determination and the right educational path, you can successfully transition to a career in AI/ML.

Robotics is a bit different field from AI/ML. Robotics has applications of AI/Ml and it will surely have some courses on the same. So what i am suggesting is, If you want to pursue career in ML/AI, its better to choose courses which fully focus on ML/AI, like MS in CS or DS. But if you love Robotics, you can pursue MS in Robotics as well, as it would help you to pursue career in Robotics as well as ML to some extent