Machine Learning for Healthcare
Massachusetts Institute of Technology via MIT OpenCourseWare
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Overview
Syllabus
1. What Makes Healthcare Unique?.
2. Overview of Clinical Care.
3. Deep Dive Into Clinical Data.
4. Risk Stratification, Part 1.
5. Risk Stratification, Part 2.
6. Physiological Time-Series.
7. Natural Language Processing (NLP), Part 1.
8. Natural Language Processing (NLP), Part 2.
9. Translating Technology Into the Clinic.
10. Application of Machine Learning to Cardiac Imaging.
11. Differential Diagnosis.
12. Machine Learning for Pathology.
13. Machine Learning for Mammography.
14. Causal Inference, Part 1.
15. Causal Inference, Part 2.
16. Reinforcement Learning, Part 1.
17. Reinforcement Learning, Part 2.
18. Disease Progression Modeling and Subtyping, Part 1.
19. Disease Progression Modeling and Subtyping, Part 2.
20. Precision Medicine.
21. Automating Clinical Work Flows.
22. Regulation of Machine Learning / Artificial Intelligence in the US.
23. Fairness.
24. Robustness to Dataset Shift.
25. Interpretability.
Taught by
Prof. Peter Szolovits and Prof. David Sontag
Tags
Reviews
4.5 rating, based on 6 Class Central reviews
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I recently completed the Machine Learning for Healthcare course, and I must say it has been a transformative experience. The course not only provided a comprehensive overview of the fundamental principles of machine learning but also demonstrated it…
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Impressive course! Comprehensive journey through ML's impact on healthcare, from clinical data analysis to AI ethics.
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Excellent free course! It exceeded my expectations. The content is highly relevant and presented in a well-structured manner, making complex concepts easy to grasp. The instructors are knowledgeable, engaging, and always ready to provide support. I…
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Good and helpful.
It is highly inspiring , it will help me a lot to do more and also love the learning techniques.
I will recommend it to all my friends and colleagues to also come and learn and add more value to there career o has to be better person in life and earn a good living. -
Delving into the nexus of healthcare and advanced machine learning, the Spring 2019 course was both comprehensive and illuminating. It adeptly showcased the transformative capacity of ML in reshaping diagnostics, treatment, and patient well-being. The hands-on assignments and projects were instrumental in fortifying pragmatic comprehension, facilitating adeptness in employing ML within medical contexts. The instructors' profound expertise, coupled with pertinent guest lectures, undeniably enhanced the overall learning expedition. In summation, an invaluable resource for those aiming to harness ML progress within the realm of healthcare.
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It contains a material rich in knowledge and I was far from these things and now I got a lot of information