| TDDE01 |
Machine Learning, 6 ECTS credits.
/Maskininlärning/
For:
CS
D
DAV
IT
U
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OBS! |
The course is not available for exchange students
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Prel. scheduled
hours: 48
Rec. self-study hours: 112
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Area of Education: Technology
Main field of studies: Computer Science, Computer Engineering
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Advancement level
(G1, G2, A): A
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Aim:
The overall aim of the course is to provide an introduction to machine learning, with special focus on regression and classification problems. Machine learning is presented from a probabilistic perspective with inference and prediction based on probability models. The course aims to give students an overview of machine learning within a unified framework and a good basis for further studies in the field.
After completing the course the student should be able to:
- use relevant concepts and methods in machine learning to formulate, structure and solve practical problems.
- infer the parameters in a number of common machine learning models.
- use machine learning models for prediction and decision making.
- evaluate and choose among models.
- implement machine learning models and algorithms in a programming language.
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Prerequisites: (valid for students admitted to programmes within which the course is offered)
Probability theory; Statistics; Mathematical analysis; Linear Algebra; Basic programming.
Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.
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Supplementary courses:
Machine learning II, Bayesian Learning, Text mining, Detection and Recognition.
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Organisation:
The course consists of lectures and computer laboratory work. The lectures introduce concepts and theories that students then use in problem solving at the computer labs.
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Course contents:
Introduction and overview of machine learning and its applications. Unsupervised and supervised learning. Discriminative and generative models. Prediction. Generalization. Classification. Nearest neighbors. Naïve Bayes. Discriminant analysis. Cross-validation. Model selection. Overfitting. Bootstrap. Regression. Regularization. Ridge regression. Lasso. Variable Selection. Binary and multi-class regression. Dimension reduction. PCA. ICA. Kernel smoothers. Support Vector Machines. Decision trees. Neural networks, Deep learning.
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Course literature:
Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2006
Hastie, T., Tibshirani, R., och Friedman J., The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2:a upplagan, Springer, 2009.
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Examination: |
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Computer examination
Laboratory work
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3 ECTS
3 ECTS
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DAT1 is an exam in a computer hall that tests students' theoretical knowledge and problem-solving skills in machine learning.
UPG1 consists of computer exercises that tests the students' ability to translate theoretical knowledge into practical problem solving in machine learning. |
Course language is English.
Department offering the course: IDA.
Director of Studies: Ann-Charlotte Hallberg
Examiner:
Course Syllabus in Swedish
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