Data Science Master's programme
Data Science Methods
The course is available to students from other degree programmes
Prerequisites in terms of knowledge
Basics of probability calculus and statistics (including multivariate probability, Bayes formula, and maximum likelihood estimators) and intermediate level linear algebra (including multivariate calculus). Good programming skills in some language and the ability to quickly acquire the basics of a new environment (R or python/numpy/scipy). Some knowledge of data science and artificial intelligence is useful but not required.
Prerequisites for students in the Data Science programme, in terms of courses
Prerequisites for other students in terms of courses
Introduction to statistics (including multivariate probability, Bayes formula, and maximum likelihood estimators). Linear algebra and matrices I-II (including multivariate calculus). TKT10002 Introduction to Programming and TKT10003 Advanced Course in Programming (i.e., good programming skills in some language and the ability to quickly acquire the basics of a new environment (R or python/numpy/scipy)).
Recommended preceding courses
DATA11001 Introduction to Data Science and DATA15001 Introduction to Artificial Intelligence
Courses in the Machine Learning module
- Defines and is able to explain basic concepts in machine learning (e.g. training data, feature, model selection, loss function, training error, test error, overfitting)
- Recognises various machine learning problems and methods suitable for them: supervised vs unsupervised learning, discriminative vs generative learning paradigm, symbolic vs numeric data
- Knows the basics of a programming environment (such as R or python/numpy/scipy) suitable for machine learning applications
- Is able to implement at least one distance-based, one linear, and one generative classification method, and apply these to solving simple classification problems
- Is able to implement and apply linear regression to solve simple regression problems
- Explains the assumptions behind the machine learning methods presented in the course
- Implements testing and cross- validation methods, and is able to apply them to evaluate the performance of machine learning methods and to perform model selection
- Comprehends the most important clustering formalisms (distance measures, k-means clustering, hierarchical clustering)
- Explains the idea of the k-means clustering algorithm and is able to implement it
- Is able to implement a method for hierarchical clustering and can interpret its results
First semester (Autumn)
Typically 2nd period
- statistical learning, models and data, evaluating performance, overfitting, bias-variance tradeoff
- linear regression
- classification: logistic regression, linear and quadratic discriminant analysis, naive Bayes, nearest neighbour classifier, decision trees, support vector machine
- clustering (flat and hierarchical); k-means, agglomerative clustering
- resampling methods (cross-validation, bootstrap), ensemble methods (bagging, random forests)
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani: An Introduction to Statistical Learning with Applications in R, Springer, 2013.
Parts of the textbook that are required are specified on the course web page.
The course will involve weekly exercises that include both programming and other kinds of problems ("pen and paper").
Assessment and grading is based on completed exercises and a course exam. Possible other criteria will be specified on the course web page.
Due to current COVID-19 situation general examinations in lecture halls are cancelled. You can check the completion method from the course page or contact the teacher to ask about alternative completion methods. - - - General exams last 3 hours and 30 minutes. Renewal exam (marked with "(U)") is the first general exam after the course and also a renewal exam of course exam(s). In a renewal exam the points student has earned during the course are taken into account. Exams marked with "(HT)" are allowed only to students who have completed the obligatory projects or other exercises included in those courses. Exams marked with "(HT/U)" are renewals to students who have completed the obligatory projects during the course. General exams might cover different area than the lectured course. Check the course web page and contact the responsible teacher if in doubt.
- Contact teaching
- Possible attendance requirements are specified each year at the course web page
- Completion is based on exercises and one or more exams. Possible other methods of completion will be announced on the course web page.