Introduction to Artificial Intelligence & Data Mining
Description
This course is focused on providing students with an introductory overview of theoretical and practical aspects of the intersection of Artificial Intelligence and Data Mining such as: data representation, pattern recognition, data classification, cluster analysis, quantification of classification performance, and data visualization. In addition, the course covers applications of interest such as biomedical data analysis, digital signal processing, image segmentation and video analysis.
Outline
Introduction to artificial intelligence
Paradigms and tasks of machine learning (ML)
Mathematical notation and basics
Time series analysis
Data representation
Performance measures
Clustering
Classification
Data visualization
Algorithmic and implementation issues
Applications
Recommended textbooks
[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] J. Lee, M. Verleysen. Nonlinear Dimensionality Reduction. Springer. here
[4] SDAS Research Group. Introduction to machine learning from linear algebra point of view. (Handout pdf)
[5] Deisenroth, Marc Peter and Faisal, A Aldo and Ong, Cheng Soon. Mathematics for machine learning. 2020 Cambridge University Press. here
[6] KB Petersen, MS Pedersen. The matrix cookbook. Technical Manual, 2012. Technical University of Denmark. here
Resources
Course information (detailed syllabus, evaluation policies, important dates)
General course instructions and guidelines
Lecture 0: Motivation and course presentation
Lecture 1: Introduction to artificial intelligence
Video (First part Second part)
Lecture 2: Paradigms and tasks of Machine Learning
Lecture 3: Mathematical notation
Scripts
2D-moving curves - toy data example (Plain Octave)
Scattering plot and data accessing - Iris dataset (MATLAB live - HTML)
Lecture 4: Time series analysis
Video (First part Second part)
Scripts
Linear Regression (Python - HTML)
SVR (Python - HTML)
Comparison of benchmark forecasting techniques (Python - HTML)
Lecture 5: Data representation
Video (First part Second part Third part)
Scripts
Lecture 6: Data classification
6.1 Linear classifier
6.2 SVM
Additional material