Fundamentals of Artificial Intelligence, Machine Learning & Robotics

UEN: 199407313C

Course Key Information

  • Eligible for Claim Period
    09 Feb 2017 - 08 Feb 2018
  • Supporting Public Agency
    SkillsFuture Singapore (SSG)
  • Training Duration
    3 days ( 24.00 hours )
  • Mode of Training
    Full Time
  • Method of Delivery
  • Course Language
  • Min. Qualification Required
    Not Specified
  • Job Level
Course Objective

This program is for executives and professionals who want to learn about the capabilities of Artificial Intelligence, Machine Learning and Deep Learning that are coming to pervade the world and workplace and are finding acceptance in almost all industries and sectors to solve many practical problems of cost management, sales growth and technological innovation. The program covers concepts, ideas and applications from leading companies and several use cases from the domain of interest to illustrate how these technologies can solve problems and create competitive advantage.. Key Takeaways: 1. An introduction to and overview of the basic techniques, and available software tools of Artificial Intelligence, Machine Learning, Deep Learning and Robotics. 2. Knowledge of the capabilities of AI and Learning for improving business performance, and some examples of their application to solve practical problems of production, logistics, marketing, management and finance in various industries and sectors. 3. More detailed knowledge of the process of formulation and deployment of AI and Machine Learning in the participant’s domain of interest, applying a use-case approach 4. A final quiz and a a capstone project designed to integrate the learning from the three-day program

Course Content

                   Artificial Intelligence  
1. Introduction  
2. History, State of the Art and Future  
3. AI Tasks: Problem solving, search, Knowledge and Reasoning, Decision Making, 
4. Learning, Perception and Communication  Artificial Intelligence  Applications  
1. Robotics  
2. Natural Language Processing  
3. Autonomous Vehicles  
4. Computer Vision  Machine Learning  
1. Introduction  
2. History, State of the Art and Future  
3. Methods: supervised, unsupervised and reinforcement  
4. Techniques: Connectionist, Evolutionary, Margin, General etc  Machine Learning  Software Tools: Python, R, Spark  Applications:   
1. Marketing  
2. Retail  
3. Finance  
4. Healthcare  Deep Learning  
1. Introduction  
2. History, State of the Art and Future  
3. Techniques  
4. Software tools
                   Artificial Intelligence  
1. Introduction  
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Course Contact

Pavan Sharma Director