Machine Learning and Medical Engineering Applications

机器学习与医学⼯程应⽤

Description

Welcome to the "Machine Learning and Medical Engineering Applications" course! Here you'll find the materials, assignments, and resources you need to dive deep into the basics of machine learning methods and their applications.

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Goals

• Totally orientated to machine learning methods (Basic ML theory and models)

  • Linear regression

  • Logistic regression

  • Support vector machine (SVM)

  • Clustering

  • Dimensionality reduction

  • Fully-connected Neural Network

  • Convolutional neural network (CNN)

• Programming, Implementation and Applications (Apply ML to medical engineering)

  • Python programming

  • Model Implementation

  • Medical applications

• Homeworks and Exams

Course Outline

1. Lecture 1 - introduction
2. Lecture 2 - linear regression
3. Lecture 3 - python tutorial
4. Lecture 4 - Frequentist vs Bayesian
5. Lecture 5 - Ridge regression
6. Lecture 6 - Logistic Regression
7. Lecture 7 - SVM
8. Lecture 8 - SVM with soft margin
9. Lecture 9 - SVM kernel method
10. Lecture 10 - PCA
11. Lecture 11 - PCA and SVD
12. Lecture 12 - LDA
13. Lecture 14 - clustering
14. Lecture 15 - Neural Network 1
15. Lecture 16 - Neural Network 2

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Contact

For any inquiries, please reach out to us at [email protected].