Please note that this workshop will take place over two days:
Tuesday, July 27, 2021: 4:00 pm – 6:00 pm
Thursday, July 29, 2021: 4:00 pm – 6:00 pm.
Essential to digital transformation strategies is the security of the organization’s digital infrastructure. This workshop aims to help the audience appreciate the benefits and requirements for data-driven intelligent decision-making in cyber security systems by providing a theoretical and practical introduction to the field of cyber security data science. Key machine learning techniques will be discussed, starting with simple exploratory data analysis (EDA) and finishing with several advanced methods including deep learning networks.
Each machine learning technique presented will be grounded in cyber security theoretical concepts and use cases to better understand data, events, and the machine learning application. Attendees will finish this workshop with an understanding of the fundamentals of machine learning and cybersecurity; appreciation for several fully working machine learning programs that may be useful for other applications; identification of further learning opportunities; and an initial portfolio of skills that is useful for in academia and/or the workplace.
This workshop is ideal for analysts, engineers, and graduate students seeking an introduction to machine learning, focusing on its application in cybersecurity. The techniques and approaches used are easily transferrable and may be applied to many data-driven industries. While this workshop requires no specific programming background or cybersecurity experience, a basic understanding of core machine learning and cyber security concepts would be beneficial.
Employees and Students at the University of Ottawa: CAD $60 + HST
General public: CAD $100 + HST
Enrolment Limited to 30 Participants
Note: Participants who successfully complete the course and satisfy the requirements for course deliverables and/or tests will receive an IEEE certificate (along with a digital badge and continuing education credits).
About the Instructor
Brad Conlin is a Ph.D. candidate at the University of Ottawa, in the Digital Transformation and Innovation program. Mr. Conlin’s research focuses primarily on the application of machine learning algorithms in cyber security. His research includes advanced neural networks and open-source data for prediction, with the application of python, Google Colab/Jupyter Notebooks, and other prominent tools and methods. He has over 15 years of experience as a data analyst and over five years in data science. Mr. Conlin has been coaching, instructing, and mentoring for close to 20 years and has won several academic, athletic and professional competitions as both a competitor and coach. Mr. Conlin is a recipient of the Queen Elizabeth II Graduate Scholarship in Science and Technology (QEII-GSST) for his research in fraud detection through SSL certificate phishing.