| Pääteemat | Esitiedot | Lähestyy oppimistavoitetta | Saavuttaa oppimistavoitteet | Syventää oppimistavoitteita |
|---|---|---|---|---|
|
Machine learning process and different types of machine learning |
- Basics of probability theory (e.g. course Introduction to statistics) and basics of linear algebra (e.g. Linear algebra and matrices I-II)
- Solves simple programming problems with some programming language and is able to quickly learn the basics of a new programming language |
- Recognizes various machine learning problems and suitable solution methods: supervised vs unsupervised learning, discriminative vs generative learning paradigm, symbolic vs numeric data
- Knows the basics of a programming environment suitable for machine learning applications |
- Defines and is able to explain basic concepts in machine learning (e.g. training data, sample, feature, hypothesis, model selection, objective function, training error, test error, overfitting)
- Solves simple data-analysis and visualization problems in a programming environment suitable for machine learning applications |
- Applies the learned concepts to solving practical problems and analyzing the solutions
- Is familiar with a variety of machine learning models in addition to those described in the course |
|
Supervised learning |
Same as for the main theme “Machine learning process and different types of machine learning” |
- Defines classification and regression problems
- Comprehends the basic concepts of supervised learning and the restrictions of the solution methods
- Is familiar with at least one distance- based, one linear, and one generative classification method
- Explains the difference between the discriminative and generative learning paradigms |
- 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 |
- Solves practical prediction problems with machine learning methods
- If needed, is able to adapt the basic methods presented in the course to practical learning problems encountered
- Is familiar with other prediction problems besides basic classification and regression (e.g. structured output prediction)
- Is familiar with other learning models such as online learning and reinforcement learning |
|
Unsupervised learning |
Same as for the main theme “Machine learning process and different types of machine learning” |
- Comprehends the most important clustering formalisms (distance measures, k-means clustering, hierarchical clustering)
- Is familiar with the problem of anomaly detection and at least one solution approach |
- Explains the idea of the k-means clustering algorithm and is able to implement it
- Explains and is able to implement a method for hierarchical clustering
- Applies the clutering methods presented in the course to simple data analysis problems
- Is able to interpret the results of hierarchical clustering methods
- Explains and is able to implement some method for anomaly detection |
- Applies clustering methods to practical data analysis problems (chooses a suitable method, applies it to the data, and analyzes the results)
- Is familiar with other unsupervised learning problems in addition to clustering and anomaly detection (e.g. density estimation) and methods for solving them |