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Created: 2025-07-20 16:56:17 UTC

Here’s a refined summary of the Intuitive Learning of Quantum Machine Learning (CSE‑41406) course at UC San Diego Extended Studies that you linked:

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📘 Course Overview
•Title: Intuitive Learning of Quantum Machine Learning (CSE‑41406)
•Mode: XXX% online
•Units: X units
•Tuition: $XXXXXX   

Aimed at professionals with AI/ML experience but no quantum mechanics background, this course teaches foundational QML algorithms through intuitive explanations and platforms like IBM Qiskit and Pennylane  .

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🎯 Learning Outcomes

By the end of the course, you will be able to:
1.Grasp the fundamental concepts of QML.
2.Understand how quantum computing can augment classical ML workflows.
and train quantum circuits for machine learning tasks using Qiskit and Pennylane.

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👩‍🏫 Instructor

Dr. James C. S. Meng – Senior Fellow at UC San Diego Supercomputer Center, former US Navy SES official, holds a Ph.D. in aeronautical engineering from UC Berkeley and an MSM from MIT Sloan. He also teaches its sibling course, “Intuitive Learning of Quantum Computing” (CSE‑41343)   .

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📌 Who It’s For

Ideal for AI/ML practitioners and data scientists interested in exploring QML methods without needing a physics-heavy background. This is an elective offering within UCSD’s Machine Learning Methods and Technical Aspects of AI certificate programs  .

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📅 When It’s Offered

It appears regularly across terms (Fall/Winter/Spring/Summer). Refer to the quarterly schedule PDF (as of Jan 29, 2025) for availability  .

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🔍 About Quantum Machine Learning (Contextual Background)

Quantum Machine Learning merges quantum computing and AI, leveraging quantum phenomena—like superposition and entanglement—to potentially accelerate classical ML tasks.
•A growing research field, including hybrid quantum‑classical approaches    .
•While promising, challenges like data encoding (“quantum feature maps”) and real‑world implementation remain under active investigation  .

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✅ Summary Table

AspectDetails
Units4 online, academic-credit units
Price$775
PrerequisitesNone in quantum theory; ML/AI familiarity preferred
Tools UsedIBM Qiskit, Pennylane
InstructorDr. James C. S. Meng
Ideal ForML practitioners seeking hands-on QML exposure

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Recommendations

If you’re intrigued by quantum-enhanced learning and want to experiment with real QML code, this structured, application-focused course is a strong fit.
If, however, you’re looking for deeper theoretical grounding—like quantum Hamiltonians, entanglement theory, or quantum complexity—you might benefit from supplementing with more physics-intensive courses or textbooks.

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Would you like help comparing this to other quantum computing or advanced ML courses? Or guidance on how it fits with one of UCSD’s certificate programs?


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