6 EC
Semester 2, period 4, 5
5123TMML6Y
Machine learning is one of the fastest growing areas of science, with far-reaching applications. This course gives an overview of the main techniques and algorithms. The lectures introduce the definitions and main characteristics of machine learning algorithms from a coherent mathematical perspective. In the workgroups the algorithms will be implemented in Python and applied to a selection of data sets.
This course is intended to bridge the gap between mathematical theory and practical applications of machine learning. As such, there is a strong emphasis on the intuition and motivation behind algorithms. Students will learn to explain the practical behavior of algorithms in terms of their theoretical properties. Attention will also be given to computational efficiency of algorithms, which is crucial when scaling up to modern big data sets.
Taking this course will be helpful if you wish to take the Machine Learning Theory course in the master program.
Contents of this course:
From supervised learning, we will cover both classical and state-of-the-art methods for classification and regression, with an emphasis on classification, including: linear discriminant analysis, the nearest-neighbor method, naive Bayes, decision trees and random forests, logistic regression, boosting, support vector machines, neural networks/deep learning, least-squares regression.
From unsupervised learning, we will cover K-means clustering and its relation to the Expectation-Maximization algorithm.
We will cover several methods for model selection: cross-validation, ridge regression and the Lasso.
From optimization, we will cover stochastic gradient descent and its application to deep learning using the backpropagation algorithm.
The Elements of Statistical Learning (ESL), 2nd edition, by Hastie, Tibshirani and Friedman, Springer-Verlag 2009.
Selected parts from Ch.18 of Computer Age Statistical Inference: Algorithms, Evidence and Data Science (CASI) by Efron and Hastie, Cambridge University Press, 2016.
A small number of handouts, which will be made available via the course website.
Activity | Hours | |
Deeltoets | 2 | |
Hoorcollege | 28 | |
Laptopcollege | 22 | |
Tentamen | 3 | |
Self study | 113 | |
Total | 168 | (6 EC x 28 uur) |
Programme's requirements concerning attendance (OER-B):
Additional requirements for this course:
Conditions for sufficient participation are: submitting serious attempts for at least 60% of the homework exercises.
Item and weight | Details |
Final grade | |
40% Final exam | |
30% Midterm | |
30% Homework |
The midterm will be about the first half of the course.
The final exam will only be about the second half of the course.
The resit exam will cover both halves; it will replace both the midterm and the final exam.
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
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The schedule for this course is published on DataNose.