PLEASE NOTE: This course will be ON-LINE

The generic course, Machine Learning and Imaging Methods (MLI-M)  introduces key analysis methods in Machine Learning and Image Analysis. These methods allow investigations of scientific data from most fields, including data from physical measurements, questionnaires, pictures, internet searches, and biochemical outcomes. We cover data cleaning (e.g. missing data, denoising), feature extraction, machine learning basics (labels, variables, parameter optimization, overfitting, cross-validation), key machine learning and image analysis methods based on both unsupervised and supervised learning, and visualization. Method-wise, we start at Linear Discriminant Analysis and end with Deep Learning.

At the end of the course, the students must write a synopsis with a suggestion for an analysis ideally performed on their own data. This synopsis could form the basis for the MLI Projects PhD course offered by the Data Science Lab in the fall.

The number of participants is limited at 55, and priority will be given to students who previously followed another Data Science Lab course (Introduction to Python or R, Statistical Methods I).

We assume that the students have some experience with Python programming.

The course is a "generic" PhD course.

After course completion the students are expected to be able to:

Knowledge:
- Understand key machine learning concepts (parameter training, overfitting).

- Understand key machine learning methods (LDA, (un-) supervised learning).

- Understand key image analysis methods (e.g. feature detection).
Skills:
- Develop/adapt/extend a computer-based software method for analysis of relevant data. 

Competences:

- Propose relevant analysis methods for scientific data science problems.
- Consider cross-disciplinary data science methods in their research.

Course lecture slides and exercises.

We will use data, examples, and other material from publicly available online sources.

The course is composed of sessions combining lectures and exercises. For each topic, the students will get hands-on experience in applying, modifying, and programming analysis methods.

The programming examples will be implemented using Python in JupyterLab notebooks.

Erik B. Dam  erikdam@diku.dk

Selvan Raghavendra  raghav@di.ku.dk