Course unit, curriculum year 2023–2024
DATA.ML.200
Pattern Recognition and Machine Learning, 5 cr
Tampere University
- Description
- Completion options
Teaching periods
Active in period 4 (4.3.2024–31.5.2024)
Course code
DATA.ML.200Language of instruction
EnglishAcademic years
2021–2022, 2022–2023, 2023–2024Level of study
Advanced studiesGrading scale
General scale, 0-5Persons responsible
Responsible teacher:
Joni KämäräinenResponsible organisation
Faculty of Information Technology and Communication Sciences 100 %
Coordinating organisation
Computing Sciences Studies 100 %
Core content
- Statistical Signal Processing: Estimation theory; Maximum likelihood; Estimation of signal parameters (e.g., phase, amplitude and frequency).
- Detection theory; Receiver Operating Characteristics; Neyman-Pearson decision rule and relation to machine learning.
- Linear models: regression and classification, support vector machines, logistic regression, regularization.
- Modern tools: Random forests, Bagging, Boosting, Stacking, Deep Learning
- Performance evaluation, cross-validation, bootstrapping
- Implementations in Python: 1) Scikit-learn, 2) Keras
Learning outcomes
Prerequisites
Compulsory prerequisites
Further information
Learning material
Equivalences
Studies that include this course
Completion option 1
Participation in teaching
04.03.2024 – 26.05.2024
Active in period 4 (4.3.2024–31.5.2024)
Completion option 2
Exam
No scheduled teaching