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Yoshua Bengio
and
Sébastien Lemieux
Ces nouvelles méthodes permettent de développer de nouveaux outils d’aide à la décision pour les professionnels du domaine de la santé, comme par exemple l’aide au diagnostic de maladies par imagerie médicale, les soins de santé personnalisés, la découverte de nouveaux médicaments ou encore une meilleure analyse des risques.
Le contenu sera présenté à l’aide de vidéos pédagogiques présentés par des experts scientifiques: Tristan Sylvain, Gaétan Marceau-Caron, Jeremy Pinto, Margaux Luck, Joseph Paul Cohen et Tariq Daouda.
Register for this course and see more details by visiting:
OpenCourser.com/course/bpapv0/science
What's inside
Learning objectives
- Développé en collaboration avec le mila et l'iric, ce cours présente :
- Les concepts fondamentaux en science des données, en apprentissage automatique et profond appliqués au secteur de la santé;
- Une introduction aux outils informatiques;
- Des applications concrètes de ces méthodes et outils à différents domaine de la santé.
- Développé en collaboration avec le mila et l'iric, ce cours présente :
- Les concepts fondamentaux en science des données, en apprentissage automatique et profond appliqués au secteur de la santé;
- Une introduction aux outils informatiques;
- Des applications concrètes de ces méthodes et outils à différents domaine de la santé.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Adapté aux professionnels de la santé ou aux étudiants intéressés à appliquer des méthodes d'IA à ce secteur
Conçu en collaboration avec le Mila et l'IRIC, des institutions reconnues en intelligence artificielle et en recherche biomédicale
Présenté par des experts scientifiques du domaine, garantissant la qualité et l'actualité des informations
Couvre une large gamme d'applications, de l'aide au diagnostic à la découverte de nouveaux médicaments
Nécessite des connaissances de base en informatique ou en mathématiques, ce qui peut être un prérequis pour certains étudiants
Save this course
Save Science des données et santé to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Science des données et santé with these
activities:
Familiarize yourself with machine learning concepts
Show steps
This course requires a basic understanding of machine learning. Following these tutorials will help you prepare.
Browse courses on
Machine Learning
Show steps
-
Go to YouTube or other video platforms and search for tutorials on machine learning.
-
Choose a tutorial that is suitable for your level of knowledge.
-
Watch the tutorial and take notes on the key concepts.
-
Try out the examples and exercises provided in the tutorial.
-
Complete at least 3 tutorials to become comfortable with the basics of machine learning.
Gather Course Materials
Show steps
Start preparing while waiting for this course to begin. Gather assigned readings and organize your classroom binder. Set up your study space.
Show steps
-
Create a dedicated course folder for handouts and assignments.
-
Purchase or download course textbooks and assigned readings.
-
Print out course syllabus and course schedule.
-
Set aside a dedicated time and space to study this course.
Review linear algebra
Show steps
Review key concepts in linear algebra to ensure a strong foundation. This will enable you to better understand the application of these concepts in data science and machine learning.
Browse courses on
Linear Algebra
Show steps
-
Go through your linear algebra lecture notes.
-
Watch online tutorials on linear algebra concepts.
-
Solve practice questions on matrices, systems of linear equations, and vector spaces.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group
Show steps
Connect with other students taking the course. Regular study sessions will enhance your understanding and provide a support system during the learning journey.
Show steps
-
Reach out to fellow students through the course discussion forum or social media.
-
Set up regular study sessions to discuss course materials, solve problems, and prepare for assessments.
Python coding exercises
Show steps
Practice writing Python code to strengthen your programming skills. This will help you implement data analysis and machine learning algorithms effectively.
Browse courses on
Python
Show steps
-
Enroll in an online Python course or coding challenge platform.
-
Solve coding exercises on data manipulation, visualization, and algorithm implementation.
Learn scikit-learn library
Show steps
Become familiar with the scikit-learn library for machine learning in Python. This will equip you with the tools necessary to build and evaluate machine learning models.
Browse courses on
scikit-learn
Show steps
-
Follow online tutorials on using scikit-learn for data preprocessing, model training, and evaluation.
-
Explore the scikit-learn documentation and API reference.
Build a machine learning model
Show steps
Apply your knowledge by building a machine learning model for a real-world dataset. This will give you hands-on experience in the entire data science workflow.
Browse courses on
Machine Learning Model
Show steps
-
Select a dataset and define the problem statement.
-
Preprocess the data and explore its characteristics.
-
Train and evaluate different machine learning models.
-
Write a report summarizing your findings and insights.
Become a course mentor
Show steps
Share your knowledge and skills by becoming a mentor for new students in the course. This will reinforce your understanding while helping others succeed.
Show steps
-
Apply to become a course mentor through the university or platform.
-
Provide guidance, support, and encouragement to new students.
-
Answer questions, facilitate discussions, and create resources for fellow learners.
Familiarize yourself with machine learning concepts
Show steps
This course requires a basic understanding of machine learning. Following these tutorials will help you prepare.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Go to YouTube or other video platforms and search for tutorials on machine learning.
- Choose a tutorial that is suitable for your level of knowledge.
- Watch the tutorial and take notes on the key concepts.
- Try out the examples and exercises provided in the tutorial.
- Complete at least 3 tutorials to become comfortable with the basics of machine learning.
Gather Course Materials
Show steps
Start preparing while waiting for this course to begin. Gather assigned readings and organize your classroom binder. Set up your study space.
Show steps
Show steps
Show steps
- Create a dedicated course folder for handouts and assignments.
- Purchase or download course textbooks and assigned readings.
- Print out course syllabus and course schedule.
- Set aside a dedicated time and space to study this course.
Review linear algebra
Show steps
Review key concepts in linear algebra to ensure a strong foundation. This will enable you to better understand the application of these concepts in data science and machine learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Go through your linear algebra lecture notes.
- Watch online tutorials on linear algebra concepts.
- Solve practice questions on matrices, systems of linear equations, and vector spaces.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group
Show steps
Connect with other students taking the course. Regular study sessions will enhance your understanding and provide a support system during the learning journey.
Show steps
Show steps
Show steps
- Reach out to fellow students through the course discussion forum or social media.
- Set up regular study sessions to discuss course materials, solve problems, and prepare for assessments.
Python coding exercises
Show steps
Practice writing Python code to strengthen your programming skills. This will help you implement data analysis and machine learning algorithms effectively.
Browse courses on
Python
Show steps
Show steps
Show steps
- Enroll in an online Python course or coding challenge platform.
- Solve coding exercises on data manipulation, visualization, and algorithm implementation.
Learn scikit-learn library
Show steps
Become familiar with the scikit-learn library for machine learning in Python. This will equip you with the tools necessary to build and evaluate machine learning models.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Follow online tutorials on using scikit-learn for data preprocessing, model training, and evaluation.
- Explore the scikit-learn documentation and API reference.
Build a machine learning model
Show steps
Apply your knowledge by building a machine learning model for a real-world dataset. This will give you hands-on experience in the entire data science workflow.
Browse courses on
Machine Learning Model
Show steps
Show steps
Show steps
- Select a dataset and define the problem statement.
- Preprocess the data and explore its characteristics.
- Train and evaluate different machine learning models.
- Write a report summarizing your findings and insights.
Become a course mentor
Show steps
Share your knowledge and skills by becoming a mentor for new students in the course. This will reinforce your understanding while helping others succeed.
Show steps
Show steps
Show steps
- Apply to become a course mentor through the university or platform.
- Provide guidance, support, and encouragement to new students.
- Answer questions, facilitate discussions, and create resources for fellow learners.
Career center
Learners who complete Science des données et santé will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/bpapv0/science
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/bpapv0/science
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Effort
5 to 7 hours per week for 3 weeks
Level
Introductory
Via
edX
Institution
Université de Montréal
Instructors
Yoshua Bengio
Sébastien Lemieux
Session
May 1, 2024
- May 1, 2025
Language
Français
Transcript
Français
Good to know
Adapté aux professionnels de la santé ou aux étudiants intéressés à appliquer des méthodes d'IA à ce secteur
Conçu en collaboration avec le Mila et l'IRIC, des institutions reconnues en intelligence artificielle et en recherche biomédicale
Présenté par des experts scientifiques du domaine, garantissant la qualité et l'actualité des informations
Couvre une large gamme d'applications, de l'aide au diagnostic à la découverte de nouveaux médicaments
Nécessite des connaissances de base en informatique ou en mathématiques, ce qui peut être un prérequis pour certains étudiants
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