This course is aimed at providing participants with mid-level training on KDD, unsupervised learning, transfer learning, imbalance data analysis, computer vision, and anomaly detection at both theoretical and practical level.
Knowledge Discovery in Data (KDD)
Data preprocessing
Machine learning for imbalanced data
Unsupervised learning
Anomaly detection algorithms
Transfer learning
Introduction to computer vision
Software and hardware architectures for algorithm implementation
[1] Ethem Alpaydın. Introduction to Machine Learning. (2nd Edition). MIT Press. Available here.
[2] Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification (2nd Edition). Wiley-Interscience. First chapter here.
[3] Deisenroth, Marc Peter and Faisal, A Aldo and Ong, Cheng Soon. Mathematics for machine learning. 2020 Cambridge University Press. here
[4] KB Petersen, MS Pedersen. The matrix cookbook. Technical Manual, 2012. Technical University of Denmark. here
Course information (outline, important dates)
Lecture 0: Motivation and course presentation
Lecture 1: Knowledge Discovery in Data (KDD)
Additional material
Lecture 2: Data preprocessing
Lecture 3: Machine learning for imbalanced data
Lecture 7: Introduction to computer vision
Scripts
MATLAB: Image segmentation