Methods and Applications for Computational Neurosciences
A.Y. 2023/2024
Learning objectives
The Course aims to provide information on modern techniques of artificial intelligence and machine learning in medicine. The students will acquire the ability to define artificial intelligence and machine learning, to describe the potential role of artificial intelligence in medicine, to explain at a basic level why data type and quality is important for use in machine learning tools, and to identify the potential use of these methods for both experimental and clinical purposes.
Expected learning outcomes
At the end of the Course, the students will achieve the following leaning outcomes:
1. Come into the methodological aspects related to the techniques of artificial intelligence and machine learning
2. Describe the learning process of neural networks
3. Interpret, with an active attitude to the themes, methods of supervised or unsupervised Learning
1. Come into the methodological aspects related to the techniques of artificial intelligence and machine learning
2. Describe the learning process of neural networks
3. Interpret, with an active attitude to the themes, methods of supervised or unsupervised Learning
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Course syllabus
AI and machine learning. Main definitions, applications and foundations. Supervised and unsupervised learning. Using AI for disease diagnosis and patient monitoring. Natural language processing. Data analytics in medicine and healthcare. Algorithm interpretability and black box approach in machine learning. AI and patient risk analysis. AI for hospital management.
Prerequisites for admission
Basic knowledge of mathematics and statistics.
Teaching methods
Teaching methods include regular face-to-face lectures, tutorials, homeworks, team working on case-studies and research papers.
Teaching Resources
Lecture notes and slides.
Repetto, M., La Torre, D., Federated Multicriteria Learning: A Goal Programming Perspective, 2022 International Conference on Decision Aid Sciences and Applications (DASA), 2022, 790-794.
Repetto, M., La Torre, D., Making It Simple? Training Deep Learning Models Toward Simplicity, 2022 International Conference on Decision Aid Sciences and Applications (DASA), 2022, 784-789.
Triberti, S., Durosini, I., Lin, J., La Torre, D., Ruiz Galán, M. On the "Human" in Human-Artificial Intelligence Interaction. Front Psychol. 2021;12:808995. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8738165/
Brownlee, J., How to use Learning Curves to Diagnose Machine Learning Model Performance, https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/
Matheny, M.E., Whicher, D., & Israni, S. T. (2020). Artificial Intelligence in Health Care: A report from the National Academy of Medicine. Jama, 323(6), 509-510. https://jamanetwork.com/journals/jama/fullarticle/2757958
Persons, T.M., U. Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning in Drug Development. https://www.gao.gov/products/GAO-20-215SP
Matheny, M.E., Whicher, D., & Israni, S. T. (2020). Artificial Intelligence in HealthCare: A report from the National Academy of Medicine. Jama, 323(6), 509-510. https://jamanetwork.com/journals/jama/fullarticle/2757958
Alvarez-Melis, David and Tommi S. Jaakkola. "A Causal Framework for Explaining the Predictions of Black-Box Sequence-to-Sequence Models." ArXiv:1707.01943, November 14, 2017. http://arxiv.org/abs/1707.01943
Athalye, Anish, Logan Engstrom, Andrew Ilyas, and Kevin Kwok. "Synthesizing Robust Adversarial Examples." Last modified June 7, 2018. http://arxiv.org/abs/1707.07397
Bahl, Manisha, Regina Barzilay, Adam B. Yedidia, Nicholas J. Locascio, Lili Yu, and Constance D. Lehman. 2018. "High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision." Radiology 286, no. 3: 810-818. https://doi.org/10.1148/radiol.2017170549
Ganin, Yaroslav, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. "Domain-Adversarial Training of Neural Networks." Last modified May 26, 2016. https://arxiv.org/abs/1505.07818
Yala, Adam, Regina Barzilay, Laura Salama, Molly Griffin, Grace Sollender, Aditya Bardia, Constance Lehman, et al. 2017. "Using Machine Learning to Parse Breast Pathology Reports." Breast Cancer Research and Treatment 161: 203-211. https://doi.org/10.1007/s10549-016-4035-1
Kabelac, Zachary, Christopher G. Tarolli, Christopher Snyder, Blake Feldman, Alistair Glidden, Chen-Yu Hsu, Rumen Hristov, E. Ray Dorsey, and Dina Katabi. "Passive Monitoring at Home: A Pilot Study in Parkinson Disease." Digital Biomarkers 3, no. 1 (April 30, 2019): 22-30. https://doi.org/10.1159/000498922
https://www.healthcareitnews.com/news/new-ai-diagnostic-tool-knows-when-defer-human-mit-researchers-say.
https://monkeylearn.com/blog/natural-language-processing-applications/
https://analyticsindiamag.com/10-nlp-open-source-datasets-to-start-your-first-nlp-project/.
Repetto, M., La Torre, D., Federated Multicriteria Learning: A Goal Programming Perspective, 2022 International Conference on Decision Aid Sciences and Applications (DASA), 2022, 790-794.
Repetto, M., La Torre, D., Making It Simple? Training Deep Learning Models Toward Simplicity, 2022 International Conference on Decision Aid Sciences and Applications (DASA), 2022, 784-789.
Triberti, S., Durosini, I., Lin, J., La Torre, D., Ruiz Galán, M. On the "Human" in Human-Artificial Intelligence Interaction. Front Psychol. 2021;12:808995. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8738165/
Brownlee, J., How to use Learning Curves to Diagnose Machine Learning Model Performance, https://machinelearningmastery.com/learning-curves-for-diagnosing-machine-learning-model-performance/
Matheny, M.E., Whicher, D., & Israni, S. T. (2020). Artificial Intelligence in Health Care: A report from the National Academy of Medicine. Jama, 323(6), 509-510. https://jamanetwork.com/journals/jama/fullarticle/2757958
Persons, T.M., U. Artificial Intelligence in Health Care: Benefits and Challenges of Machine Learning in Drug Development. https://www.gao.gov/products/GAO-20-215SP
Matheny, M.E., Whicher, D., & Israni, S. T. (2020). Artificial Intelligence in HealthCare: A report from the National Academy of Medicine. Jama, 323(6), 509-510. https://jamanetwork.com/journals/jama/fullarticle/2757958
Alvarez-Melis, David and Tommi S. Jaakkola. "A Causal Framework for Explaining the Predictions of Black-Box Sequence-to-Sequence Models." ArXiv:1707.01943, November 14, 2017. http://arxiv.org/abs/1707.01943
Athalye, Anish, Logan Engstrom, Andrew Ilyas, and Kevin Kwok. "Synthesizing Robust Adversarial Examples." Last modified June 7, 2018. http://arxiv.org/abs/1707.07397
Bahl, Manisha, Regina Barzilay, Adam B. Yedidia, Nicholas J. Locascio, Lili Yu, and Constance D. Lehman. 2018. "High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision." Radiology 286, no. 3: 810-818. https://doi.org/10.1148/radiol.2017170549
Ganin, Yaroslav, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. "Domain-Adversarial Training of Neural Networks." Last modified May 26, 2016. https://arxiv.org/abs/1505.07818
Yala, Adam, Regina Barzilay, Laura Salama, Molly Griffin, Grace Sollender, Aditya Bardia, Constance Lehman, et al. 2017. "Using Machine Learning to Parse Breast Pathology Reports." Breast Cancer Research and Treatment 161: 203-211. https://doi.org/10.1007/s10549-016-4035-1
Kabelac, Zachary, Christopher G. Tarolli, Christopher Snyder, Blake Feldman, Alistair Glidden, Chen-Yu Hsu, Rumen Hristov, E. Ray Dorsey, and Dina Katabi. "Passive Monitoring at Home: A Pilot Study in Parkinson Disease." Digital Biomarkers 3, no. 1 (April 30, 2019): 22-30. https://doi.org/10.1159/000498922
https://www.healthcareitnews.com/news/new-ai-diagnostic-tool-knows-when-defer-human-mit-researchers-say.
https://monkeylearn.com/blog/natural-language-processing-applications/
https://analyticsindiamag.com/10-nlp-open-source-datasets-to-start-your-first-nlp-project/.
Assessment methods and Criteria
The assessment (for attending students) Is baded on in-class activties (group presentations) and a final MCQ test. The assessment for non-attending students is only based on a written exam with both MCQ and open questions.
Educational website(s)
Professor(s)