physdm

Data mining and machine learning course for physics MSc students

Lecture slides:

• 00 Requirements

• 01 Unsupervised learning and clustering

• 02 Overview of supervised learning

• 03 Linear regression

• 04 Linear methods for classification

• 05 Model selection and regularization

• 06 SVM

• 07 Decision trees

• 08 Neural networks

• 09 Convolutional neural networks

• 10 Neural networks II.

• 11 Generative adversarial networks

• 12 Natural language processing,nb1,nb2

Lab assignments

• 01 Unsupervised learning and clustering

• 02 Overview of supervised learning

• 03 Linear regression

• 04 Linear methods in classification

• 05 Model selection and regularization

• 06 SVM

• 07 Decision trees

• 08 Neural networks

• 09 Convolutional neural networks

• 10 Neural networks II.

• 11 Generative adversarial networks

Formal requirements for assigments.

Solution for this year (quick and dirty, do not expect explanations)

• 01 Unsupervised learning and clustering
• 02 Overview of supervised learning
• 03 Linear Regression

• 04 Linear methods in classification

Worked out example notebooks, old assignment solutions

• 01 Unsupervised learning and clustering
• 02 Overview of supervised learning
• 03 Linear Regression