6 EC
Semester 1, period 2
5294APML6Y
Machine learning is marking a revolution in the world. From an academic research topic, over the last decade it has shift to a major paradigm used in many companies for a wide range of services. From deleting SPAM mail from your inbox to ranking the Google search results, and from defining your Facebook stream to enabling medical diagnoses.
In this course we study learning from large collections of unstructured data, such as text documents, web pages, images and videos. We address the complete machine learning chain, from designing the system and its objectives, to representing data and selecting and evaluating the learning method. We review and focus on the foundations of supervised classification and regression, which we extend to deep learning. We have application lectures on text processing, computer vision and recommender systems.
You will learn the theoretical concepts during the lectures with a keen eye on the design of the full learning system. In the tutorials we will focus on some off the important mathematical concepts, and in the lab you will gain hands-on experience through a number of coding assignments (implementing your own deep learning functions) and by participating in a Kaggle competition or project. Finally, a few experts from the field (both academic as well as industry colleagues) are invited to provide guest lectures.
A syllabus containing articles and chapters will be made available at the beginning of the course.
The syllabus/course will cover parts of the following books
1. Introduction to Statistical Learning (with Applications in R), by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, Springer, 2013, see http://www-bcf.usc.edu/~gareth/ISL/
2. Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville MIT Press, 2016, see http://www.deeplearningbook.org
3. Recommender Systems, by Charu Aggarwal Springer, 2016 , see http://charuaggarwal.net/Recommender-Systems.htm
Activity | Hours | |
Hoorcollege | 28 | |
Laptopcollege | 14 | |
Tentamen | 3 | |
Werkcollege | 14 | |
Self study | 109 | |
Total | 168 | (6 EC x 28 uur) |
In TER part B of this programme no requirements regarding attendance are mentioned.
| Item and weight | Details |
|
Final grade | |
|
5% Individual lab assignment | Must be ≥ 5.5, NAP if missing |
|
40% Final Project | Must be ≥ 5.5, NAP if missing |
|
55% aml2425-exam | Must be ≥ 5.5, NAP if missing |
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
| Weeknummer | Onderwerpen |
| 1 | Regression |
| 2 | Classification |
| 3 | Deep learning |
| 4 | Deep Learning |
| 5 | Evaluation and Fairness |
| 6 | Applied Lectures |
| 7 | Advanced Machine Learning |
| 8 | Poster session and Exam |
Note: This course is not designed for MSc AI students since it covers aspects from Machine Learning 1 and Computer Vision 1 and therefore can't be used as elective.