Course manual 2020/2021
Course content
The course will be "hands-on". Lectures are interspersed with coding exercises, to give students enough time to digest and apply new concepts immediately in practice. The goal is that students not only understand the theoretical foundations of the most important machine learning techniques, all the way to modern developments in deep learning, but also that they acquire experience in applying this technology to research problems from the physics and astronomy domain.
The content of the course is still in the design phase. The current plan is to cover:
- Probability
- Probability theory
- Common distributions
- Transformation and combination of random variables
- Monte Carlo approximations
- Information: Entropy, maximum entropy distributions
- Bayesian and Frequentist decision making
- Frequentist & Hypothesis testing (p-value etc)
- Maximum likelihood method, confidence intervals
- Bayesian theorem & model selection
- MAP estimation, credible intervals
- Basic Machine Learning Methods
- Linear regression & ridge / regularized regression
- Logistic regression & model fitting (gradient descent)
- Latent linear models (Factor analysis, Principle Component Analysis)
- Kernel estimation, Support Vector Machines
- Clustering techniques (K-means, mixture models, hierarchical clustering)
- Deep neural networks
- Neural networks
- Classification and regression networks
- Image classification & generative models
- Likelihood-free inference and posterior estimation
Study materials
Literature
Other
Objectives
- Understand and apply probability calculus
- Apply Bayesian and Frequentist decision making techniques
- Application of standard ML techniques (e.g. linear regression, logistic regression, kernel estimation, clustering, PCA)
- Theory and application of neural networks (classification/regression, generative models, likelihood-free inference)
- Create and optimize neural network structures for toy learning problems
Teaching methods
- Lecture
- Laptop seminar
- Self-study
- Seminar
Learning activities
Activity | Hours |
Hoorcollege | 22 |
Laptopcollege | 22 |
Tentamen | 3 |
Self study | 121 |
Total | 168 | (6 EC x 28 uur) |
Attendance
Requirements concerning attendance (OER-B).
In addition to, or instead of, classes in the form of lectures, the elements of the master’s examination programme often include a practical component as defined in article A-1.2 of part A. The course catalogue contains information on the types of classes in each part of the programme. Attendance during practical components is mandatory.
Assessment
- The exam has to be passed in order to pass the course.
- Graded exercises will contribute 40% to the grade, both for the exam and retake.
Fraud and plagiarism
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
Course structure
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The schedule for this course is published on DataNose.
Course details will be made available on Canvas.
Coordinator