Calendar

Apr
30
Mon
SORA Business Analytics Seminar – Real World Application of AI with Case Studies @ RBC Waterpark Place Auditorium
Apr 30 @ 17:30 – 18:30
SORA Business Analytics Seminar - Real World Application of AI with Case Studies @ RBC Waterpark Place Auditorium | Toronto | Ontario | Canada
Click here to Register!

DESCRIPTION

This talk will give an overview of Artificial Intelligence from a practitioner who has been a leader in the analytics world for many years. The talk will cover the following:

  1. Provide a definition of AI. A term that is very misunderstood and misused in the market place.
  2. Discuss the missing elements in the current conversation about AI that are required to delivered applied solutions.
  3. Provide Retail and Finance Industry AI use cases that Daisy is delivering to clients today.
  4. Share case study results for Daisy use cases.

About Gary:

Gary is an expert on AI technology and evangelizes about the societal benefits of AI and machine learning as it pertains to improving business decision making. Gary’s love of math and science started with punch cards and parallel computing meant driving back and forth to computers at all his friend’s houses to run jobs to get his computational aerospace assignments completed.

Gary is the former head of IBM Canada’s analytics and data warehousing practices and previously the head of Loyalty Consulting Group, providing analytical and data management services for one of the world’s most successful coalition loyalty programs. Gary holds both a BASc and MASc in aerospace engineering from the University of Toronto and continues to lecture and advise in curriculum development for the Engineering Science and AI programs at U of T.

Gary is the founder and CEO of Daisy Intelligence which has a proven record-of-success applying A.I. powered technology to automated core business decisions. Daisy’s software-as-a-service (SaaS) platform analyzes 100% of input data, simulates the possible alternatives and trade-offs inherent in any complex business question, considers any boundary conditions and constraints, and recommends the optimal course of action to best meet a client’s goals.

May
8
Tue
TABA seminar: Bayesian Network for Causal Analysis by Fei Wang, McDougall Scientific @ Room 7-605, Princess Margaret Hospital
May 8 @ 18:00 – 19:00
Jun
15
Fri
SORA-TABA-DLSPH Workshop @ Dalla Lana School of Public Health, 6th Floor Auditorium, HS610
Jun 15 @ 09:00 – 17:00

For more information visit SORA-TABA-DLSPH Workshop 2018.

Sep
14
Fri
SORA Business Analytics Seminar #3 – The Role of Geography in Data Integration and Predictive Analytics @ RBC Waterpark Place Auditorium
Sep 14 @ 08:30 – 10:00

The role of geography in data integration and predictive analytics 

 Tony Lea, Ph.D. Senior VP and Chief Methodologist, Environics Analytics

In a world where data governance and privacy concerns make it increasingly unable to use data on individuals it is important to have an alternative that works. This paper explains how the use of aggregated data for small scale geography can provide much of the solution in a privacy friendly way. This is called geodemography. After working through some theory, an example is presented that shows how spatial autocorrelation and segmentation can facilitate effective data integration and powerful predictive models. This approach allows users to have thousands of potential predictive variables – socioeconomic and demographic, behavioural, psychographic etc. – beyond those available from customer transactional and tombstone files Not only does this provide better targeting tools but equally important the ability to generate effective marketing communication strategies.

Nov
19
Mon
Machine Learning Model Testing @ Royal Bank Waterpark Place - Auditorium
Nov 19 @ 17:30 – 18:30
Machine learning
Model testing and validation for production-level systems
Leo Guelman
Chief Statistician and Director, Data Science
Royal Bank of Canada

Building reliable machine learning (ML) models for use in production-level systems present specific risk factors not commonly addressed in the practitioners’ literature. As ML continues to play a central role in decision making processes, it is critical to evaluate models under several conditions to identify potential defects. In this session, we introduce model testing and validation methods to ensure the production-readiness of a model. In particular, we discuss concepts such as statistical model criticism, performance uncertainty, model staleness, calibration, algorithmic bias, and interpretability. The underlying methods to tackle these issues are essential for the long-term health of ML production systems, and should be seamlessly integrated in data science pipelines.

 

Feb
19
Tue
Panel Discussion – Analytics Consulting – How to drive business value as an external resource @ RBC Waterpark Auditorium
Feb 19 @ 18:00 – Feb 19 @ 19:30
Mar
28
Thu
SORA Seminar – An Overview of the Techniques of Predictive Modeling in Marketing @ Ryerson University Engineering Bldg, RM 103
Mar 28 @ 17:30 – 19:30
SORA Business Analytics Seminar Toronto
Seminar
An Overview of the Techniques of Predictive Modeling in Marketing

Featuring Michael Vainder
Vice President, Modelling, Environics Analytics

 

May
3
Fri
SORA-TABA-DLSPH Workshop 2019 @ Jackman Law Building, University of Toronto
May 3 all day

Topic:  Regression Modelling Strategies
Instructor:  Frank Harrell, Vanderbilt University

Frank Harrell

May
29
Wed
Best Practices and Solutions for Operational AI systems, Asmita Usturge, Microsoft @ RBC Waterpark Auditorium
May 29 @ 17:30 – 19:30
Operationalizing Machine Learning, Asmita Usturge, Microsoft @ RBC Waterpark Auditorium
May 29 @ 17:30 – 19:30

Asmita Usturge, Data Scientist | Microsoft AI

Asmita is a Data Scientist/ Cloud Solution Architect.   She is a passionate data science developer and enabler of cloud technologies for AI.  She works with Microsoft’s high potential customers like financial banks in Toronto to help them transform AI application on Azure cloud platform.    She has 12+ years of experience in the field of computer science, statistics, machine learning and development of AI systems.

The talk:

The AI field is young compared to traditional software development. Best practices and solutions around life cycle management for these AI systems have yet to solidify. This talk will discuss challenges, best practices and technology to operationalize scalable AI models in enterprise