Introduction to Machine Learning
Students will learn how to use Python for data processing. In particular, they will be able to use machine learning and statistical methods for data analysis. Moreover, they will learn the foundation of each statistical methods.
Learn how to use machine learning methods for real data. Beginning with the basic of machine learning including linear algebra, probability, linear regression, and logistic regression, and progressing to deep learning methods. In addition to the lectures, hands-on classes develop competencies in practical use of these techniques. Finally, implement these in student-driven machine learning projects (possibly using data provided from OIST units).
Two weekly sessions:
1. Introduction to Machine Learning; Introduction to Python
2. Linear Algebra for ML; Vectors and Matrices
3. Probability and Maximum Likelihood estimation; Maximum likelihood estimation (Hands-on)
4. Linear Regression; Linear Regression (Hands-on)
5. Mid-term exam; Review of Mid-term exam
6. Classification; Classification (Hands-on)
7. Nonlinear Regression; Nonlinear Regression (Hands-on)
8. Feature Selection; Feature Selection (Hands-on)
9. Dimensionality Reduction (PCA, CCA, t-SNE); Dimensionality Reduction (Hands-on)
10. Introduction to Deep Learning; Introduction to Deep Learning (Hands-on)
11. Project 1; Project 2
12. Project 3; Project 4
13. Final presentation 1; Final presentation 2
In-term tests 30%, project 70%
We will teach about Python, basic linear algebra, and probability. However, prior knowledge of these topics is highly recommended.
Mathematics for Machine Learning https://mml-book.github.io/
Pattern Recognition and Machine Learning https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pa…
Deep Learning https://www.deeplearningbook.org/
Foundations of Machine Learning https://cs.nyu.edu/~mohri/mlbook/