Formation Artificial Intelligence and Machine Learning in E-Governance au canada

Training :Artificial Intelligence and Machine Learning in E-Governance

Course Objectives:
Up on completion of this course, the student will:
1. Gain advance knowledge of AI and machine learning, and their applications with a focus on e-governance.
2. Understand machine learning, deep learning, neural networks models.
3. Have the ability to implement AI and machine learning in e-governance through several case studies.
4. Have the ability to identify and assess the possibilities for AI in his/her organisation.

Course Content:
MODULE 1 : Artificial Intelligence
Overview of Artificial Intelligence (AI) and its applications, limitations and challenges within the context of the digital ecosystem.
MODULE 2 : AI and Machine Learning
Understand the Mechanics of the three main types of machine learning: supervised, reinforcement, and unsupervised learning. Discuss best practices for applying machine learning in practical problems including e-governance and learn the best ways to evaluate performance of the learned models.
MODULE 3 : Neural Networks and Deep Learning
Neural networks is a model inspired by how the brain works. It is widely used today in many applications including e-governance. Understand what deep learning is and how it is powering the modern approach to AI.
MODULE 4 : Large Scale Machine Learning
Machine learning works best when there is an abundance of data to leverage for training. We discuss how to apply the machine learning algorithms with large datasets.
MODULE 5 : AI for E-Governance
Identify the value of AI in e-governance, transform processes in e-governance with AI – Case study, and responsible AI in e-governance.
MODULE 6 : AI for Citizen Services Use Cases
AI use cases for citizen inquiries and information, filling out and searching documents,
routing requests, translation, and drafting documents

Required texts:
Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig
Publisher: Pearson; 3 edition (Dec 1 2009)
Reinforcement Learning: An Introduction
by Richard S. Sutton and Andrew G. Barto Publisher: A Bradford Book; 1 edition (Feb. 26 1998)
(Covers Markov decision processes and reinforcement learning. Available free online.)
The Elements of Statistical Learning
by Trevor Hastie, Robert Tibshirani, Jerome Friedman
Publisher: Springer; 2nd ed. 2009, Corr. 9th printing 2017 edition (April 21 2017)
(Covers Machine learning. Available free online.)
Reference texts: The Master Algorithm
by Pedro Domingos
Publisher: Basic Books; 1 edition, (September 22, 2015)
by Nick Bostrom
Publisher: Audible Studios on Brilliance Audio; MP3 Una edition (May 5, 2015)
Foundations of constraint satisfaction
by Edward Tsang and Thom Fruehwirth
Publisher: Books on Demand (May 13 2014)
(Covers constraint satisfaction problems. Available free online)

Extra Modules to have an comprehensive program in AI:

• Robotics in Business
• Artificial Intelligence in Business and Society
• The Future of Artificial Intelligence
• Natural Language Processing in Business
• Intelligent Systems
• Computational Theory Building for Education Computers and Games
• Non-Procedural Programming Languages
• Artificial Intelligence and Video Games
• Advanced Game Programming
• Game Design Principles and Practice