Alongside Prof. Krisztian Balog, I co-lectured the M.Sc. course DAT630 - Web Search and Data Mining, on Fall 2017, at University of Stavanger. I was responsible for lecturing the Data Mining part (essentials of Machine Learning), and leading its practicum, as well as in charge of the laboratory for the entire course.
The book used for this part is Introduction to Data Mining, by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar (Pearson, 2006).
Below, you can find my slides for each lecture, and the book chapter they correspond to.
Introduction and Data
This series of lectures starts by introducing the motivations and challenges in data mining, the types of data, problems and criteria on data quality, techniques for data preprocessing, and measures.
This lecture corresponds to the chapters 1 and 2 of the book.
Here, we present core statistics for exploring data, as well as visualization techniques.
This lecture corresponds to the chapter 3 of the book.
We introduce the problem of supervised machine learning, and detail fundamental learning algorithms and concepts for evaluation.
This lecture corresponds to the chapter 4 of the book.
Having studied the basics of classification and a handful of methods, in this lecture we discuss alternative classification techniques and additional concepts.
This lecture corresponds to the chapter 5 of the book.
In the final lecture, we move on to unsupervised learning, and describe the essentials of clustering, its types, and distinguished clustering methods.
This lecture corresponds to the chapter 8 of the book.