Artificial Intelligence revolution is the development of computer systems able to perform tasks normally requiring human intelligence. When you encounter awe-inspiring technology, it can be challenging to see how you can apply it to your own business. You need a framework and language to ask deeper, sharper questions and find the right strategy.
This four-day Artificial Intelligence Course has been designed to develop the insight of the participants on Data Science. Participants on this training course will learn how to build Artificial Intelligence, and to optimize the Artificial Intelligence to reach its maximum potential. The training modules will enable the participants to understand the theory of Artificial Intelligence and help them to understand how to solve real-world problems with Artificial Intelligence.
Participants attending Artificial Intelligence training course will develop the following competencies:
- Understand the value of Artificial Intelligence and different algorithms
- Learn how to analyze daily business problems and create Artificial Intelligence solutions
- Gain a deeper understanding of the latest innovative technologies and apply them to business
Who Should Attend?
The target audience for this course include professionals who are interested in learning robotics and biometrics. This EuroMaTech training course is also meant for people who are very keen about learning Artificial Intelligence and its implementation.
The topics included in this EuroMaTech training course are related to Probability Theorem and Linear Algebra. Therefore a basic knowledge in statistics and mathematics will be an added advantage for participants wanting to take this course.
By completing this course, the candidates will be able to:
- How Artificial Intelligence can be optimized so that the maximum potential could be obtained.
- The beginners learn how the codes come is combined and what lines mean?
- Understanding the procedure of building the Artificial Intelligence
- Understanding how could a trainee provide support to the Data Scientist
- Learn how to build Artificial Intelligence that is adaptable to any environment in real life
- Identify potential areas of applications of Artificial Intelligence
- Basic ideas and techniques in the design of intelligent computer systems
- Statistical and decision-theoretic modelling paradigm
- How to build agents that exhibit reasoning and learning
- Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
This Artificial Intelligence training course will combine presentations with instructor-guided interactive discussions between participants relating to their individual interests. Practical exercises, video material and case studies aiming at stimulating these discussions and providing maximum benefit to the participants will support the formal presentation sessions. Above all, the course leader will make extensive use of case examples and case studies of issues in which he has been personally involved.
This unique EuroMaTech training course on Artificial Intelligence covers discussion of critical areas of business problem solving and will have above all a practical focus on decision-making.
The focus of this training course is on the actions required to achieve effective design and introduction of suitable solutions. This will include detailed presentation and discussion of contemporary leading-edge approaches to strategic planning, showing how standard solutions should be modified to reflect intelligence which helps in saving time, money and effort.
Day 1 - Overview of Artificial Intelligence
- Introduction to Artificial Intelligence
- Intelligent Agents
Representation and Search: State Space Search
- Information on State Space Search
- Graph theory on state space search
- Problem Solving through state space search
- DFS algorithm
- DFS with iterative deepening (DFID)
- Backtracking algorithm
Day 2 - Representation and Search: Heuristic Search
- Heuristic search overview
- Pure Heuristic Search
- A* Algorithm
- Iterative- Deepening A*
- Depth First Branch and Bound
- Heuristic Path Algorithm
- Simple hill climbing
- Best first search algorithm
- Admissibility heuristic
- Min Max algorithm
- Alpha beta pruning
- Machine learning overview
- Perceptron learning and Neural networks
- Updating the weights of Neural networks
- Clustering algorithms
Day 3 - Logics and Reasoning
- Logic reasoning overview
- First Order Predicate Calculus (FOPC)
- Modus ponens and Modus tollens
- Unification and deduction process
- Resolution refutation
Rule Based Programming
- Production system
- CLIPS installation and clips tutorial
Day 4 - Decision Making
- Intelligent agent
- Generic agent
- Autonomous agent
- Reflex agent
- Goal based agent
- Utility based agent
- Decision theory
- Decision network
- Reinforcement learning
- Markov Decision Processes (MDP)
- Dynamic Decision Networks (DDN)
- Basics of set theory
- Probability distribution
- Bayesian rule for conditional probability