All sessions will take place in Engineering Block G (ENG) and Engineering Block B (ENB). Please click on each session title to get detailed information for that course: abstract, prerequisites, software requirements (if any).

date and time	ENG 124/130 (Stream 1)	ENB 201 (Stream 2)	ENG 024 (Machine Learning)
Mon 27th morning 9:00am-12:00pm	Bash Scripting and Tools (with intro to ARC) by Alex Razoumov		• 9am-10:15am Introduction to Python by Abed Sarhan • 10:30am-12pm Regression Classifiers with Python by Abed Sarhan
Mon 27th afternoon 1:00pm-4:00pm	Working with Text by Ian Percel	Introduction to Apache Spark by Dave Schulz	Nearest Neighbours in Python with scikit-learn by Jean Auriol
Tue 28th morning 9:00am-12:00pm	Introduction to HPC (part 1) by Alex Razoumov	Introduction to C (and pointers) for Python Programmers by Dmitri Rozmanov	Gathering and Using Unstructured Data by ATB Financial
Tue 28th afternoon 1:00pm-4:00pm	Introduction to HPC (part 2) by Alex Razoumov		Application of Data Science to Finance by ATB Financial
Wed 29th morning 9:00am-12:00pm	Scientific Visualization (part 1) by Alex Razoumov	Selected Python libraries for Researchers by Ian Percel	• 9am-10:15am Artificial Neural Networks in Python by Yasaman Amannejad • 10:30am-12pm ANFIS with Python by Yasaman Amannejad
Wed 29th afternoon 1:00pm-4:00pm	Scientific Visualization (part 2) by Alex Razoumov	Speeding up Python code with C/C++ by Dmitri Rozmanov	Practical Applications of Deep Learning with MATLAB by Reece Teramoto
Thu 30th morning 9:00am-12:00pm	Chapel parallel programming by Alex Razoumov	R in HPC environment by Mark Lowerison	• 9am-10:15am K-means Clustering in Python by Yasaman Amannejad • 10:30am-12pm Principal Component Analysis for Feature Selection by Yasaman Amannejad
Thu 30th afternoon 1:00pm-4:00pm	MATLAB Parallel Computing by Sam Marshalik	Working with spatial data by Ian Percel	Machine Learning: real data issues, how to handle them by Giovane Cesar da Silva