|Website:||website containing additional information|
|Period:||period 2 (week 46 through 5, i.e., 12-11-2018 through 1-2-2019; retake week 16)
|Participants:||up till now 85 subscriptions|
|Schedule:||Official schedule representation can be found in Osiris|
|Contents:||Knowledge of elementary probability theory, statistics,
multivariable calculus and linear algebra is presupposed.
In this course we study statistical pattern recognition and machine learning.
The subjects covered are:
For questions about enrollment, registration, waiting lists,
admittance, etc. please contact the student desk at email@example.com.
- General principles of data analysis: overfitting, bias-variance trade-off, model selection, regularization, the curse of dimensionality.
- Linear statistical models for regression and classification.
- Clustering and unsupervised learning.
- Support vector machines.
- Neural networks and deep learning.
- Book: Christopher M. Bishop, Pattern Recognition and Machine Learning,
- Book: Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning,
MIT Press, 2016.
Book URL: http://www.deeplearningbook.org.
- Possibly additional literature in the form of research papers,
book chapters, etc.
|Course form:||Lectures and computer lab sessions.|
|Exam form:||Written exam and practical assignments.|
|Minimum effort to qualify for 2nd chance exam:|| |