Program

26 June
08:30 – 09:00	Registration
09:00 – 11:00	Lecture 1. Intro to Data Science; Classical Statistical Inference; Regression:  James-Stein Estimation and Ridge Regression, The Jackknife and the Bootstrap, Bootstrap Confidence Intervals, Sparse Modeling and the Lasso. Answers
	Lecturer: Dr. Christof Seliler
11:00 – 11:30	Coffee Break
11:30 – 13:00	Lab 1. Regression in R
	Lecturer: Dr. Christof Seliler
13:00 – 14:00	Lunch Break
14:00 – 16:00	Lecture 2. Classification: Decision Trees, Nearest Neighbour Classification,  Logistic Regression, and Support Vector Machines
	Lecturer: Dr.ir. Kurt Driessens
16:00 – 16:15	Coffee Break
16:15 – 17:45	Lab 2. Classification and Overfitting in Weka Answers
	Lecturer: Dr.ir. Kurt Driessens
27 June
09:00 – 11:00	Lecture 3. Clustering:  k-means, Hierarchical Clustering, DBSCAN, and Validation [ppt, pdf]
	Lecturer: Dr. Jerry Spanakis
11:00 – 11:30	Coffee Break
11:30 – 13:00	Lab 3. Clustering in Weka Weka and Data for Lab 3
	Lecturer: Dr. Jerry Spanakis
13:00 – 14:00	Lunch Break
14:00 – 16:00	Lecture 4.  Validation of Supervised Models: Hold-out Validation, Cross Validation, ROC Analysis; Feature Selection: Filters, Wrappers, Embedded Methods
	Lecturer: Dr. Enrique Hortal
16:00 – 16:15	Coffee Break
16:15 – 17:45	Lab 4. Validation of Supervised Models and Feature Selection in Weka Data
	Lecturer: Dr. Enrique Hortal
28 June
09:00 – 11:00	Lecture 5. Association Rule Mining: Apriori, Frequent Item Mining, Rule Generation
	Lecturer: Dr. Mirela Popa
11:00 – 11:30	Coffee Break
11:30 – 13:00	Lab 5. Association rule mining    Data
	Lecturer: Dr. Evgueni Smirnov
13:00 – 14:30	Lunch Break
14:30 – 16:00	Lecture 6. Deep Learning: Classical Feedforward Neural Nets, Convolutional Neural Networks, Regularization, Recurrent Neural networks, and GANs
	Lecturer: Dr. Siamak Mehrkanoon
16:00 – 16:15	Coffee Break
16:15 – 17:45	Lab 6. Deep Learning in Python
	Lecturer: Dr. Siamak Mehrkanoon