Best Artificial Intelligence Course in Muntinlupa | Artificial Intelligence Training in Muntinlupa

Artificial Intelligence Course Muntinlupa

“Artificial Intelligence Course Muntinlupa” |”Artificial Intelligence Training Muntinlupa” | Artificial intelligence (AI) is the simulation of human intelligence through machines & mostly through computer systems. Artificial Intelligence Course Muntinlupa is a subfield of the computer. It allows computers to do things which are normally done by human beings. Any program can be said to be Artificial intelligence if it is able to do something that the humans do it using their intelligence through AI Programming Language. AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future. Learn Artificial Intelligence Course Muntinlupa by the industry experts, the program is conducted by Mildaintrainings.

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  • ✔ Course Duration : 48 hrs
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Artificial Intelligence Course Muntinlupa

Artificial intelligence Course Muntinlupa (AI) is the simulation of human intelligence through machines & mostly through computer systems. Artificial Intelligence Course Muntinlupa is a sub field of computer. It allows computers to do things which are normally done by human beings. Any program can be said to be Artificial intelligence Course Muntinlupa if it is able to do something that the humans do it using their intelligence. In other words, Artificial Intelligence means the power of a machine to copy the human intelligent behavior. It is all about designing machines that can think.

Obviously, there is a lot more to it. AI is a broad topic ranging from simple calculators to self-steering technology to something that might radically change the future.

What you will Learn

After completion of AI course you will be able to:

  • Identify potential areas of applications of AI
  • Basic ideas & techniques in the design of intelligent computer systems
  • Statistical & decision–theoretic modeling paradigm
  • How to build agents that exhibit reasoning & learning
  • Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.


The topics included in this topic will be related to probability theorem and linear algebra. So a basic knowledge in statistics and mathematics is an added advantage to take up this course. Technical background is a must.


Learning Objectives:
  • Introduction to Artificial Intelligence
    • History of artificial intelligence
    • Detailed explanation of Artificial intelligence with a definition and meaning.
    • Why artificial intelligence is important in today’s world?
    • What is involved in artificial intelligence?
    • The academic disciplines which are related to artificial intelligence.
  • Intelligent Agents
    • What is intelligent agents?
    • Agents and environment
    • Concept of rationality
    • Types of agents – Generic agent, Autonomous agent, Reflex agent, Goal Based Agent, Utility based agent
Learning Objectives:
  • Information on State Space Search
    • Introduction to State Space Search in artificial intelligence, representation.
    • Components of search systems.
    • The areas where state space search is used.
  • Graph theory on state space search
    • What is a graph theory?
    • How may graph theory be used to model problem solving as a search through a graph of problem states?
    • The And-Or graph is explained with its uses.
    • Introduction on components of the graph theory.
  • Problem-Solving through state space search
    • General Problem, Variants, types of problem-solving approach is explained.
  • DFS algorithm
    • Depth First Search searches deeper into the problem space.
    • Advantages, disadvantages and algorithm of depth first search.
  • DFS with iterative deepening (DFID)
    • What iterative deepening search?
    • Combination of breadth first search and depth first search.
    • Its properties & algorithm along with examples.
  • Backtracking algorithm
    • What is backtracking?
    • Implementation of Artificial Intelligence.
    • Description of the methods
    • When backtracking can be used?
    • For what applications backtracking algorithm can be used.?
Learning Objectives:
  • Heuristic search overview
    • Heuristic search: Rule of thumb
    • Heuristic search: Search strategies.
    • function of the nodes and the goals.
    • The general meaning and the technical meaning of Heuristic search.
    • Heuristic search: Function of the nodes and the goals.
    • Heuristic search techniques: Pure Heuristic Search
    • Heuristic search techniques: A* Algorithm
    • Heuristic search techniques: Iterative- Deepening A*
    • Heuristic search techniques: Depth First Branch and Bound
    • Heuristic search techniques: Heuristic Path Algorithm
    • Heuristic search techniques: Recursive Best-First Search
  • Simple hill climbing
    • Simple Hill Climbing technique in Heuristic search.
    • Function optimization of hill climbing.
    • Problems with simple hill climbing and its example.
  • Best-first search algorithm
    • Combined advantages of breadth first and depth first searches.
  • Admissibility Heuristic
    • What is admissibility?
    • Heuristic, its formulation, construction
    • Admissible heuristic using a puzzle problem.
    • How to estimate the cost to reach the goal state?
  • Min-Max algorithm
    • Introduction to the Min-Max algorithm.
    • Explanation of the two players MIN and MAX.
    • Use of Min-Max Algorithm in two-player games such as Chess and others.
    • Introduction to search trees.
    • Optimization
    • Speeding the algorithm
    • Adding alpha beta cut-offs
  • Alpha-beta pruning
    • The Alpha value of the node.
    • The beta value of the node.
    • Improvements over minimax algorithm.
    • Pseudo code and a detailed game example.
Learning Objectives:
  • Machine learning overview
    • Introduction about the Machine learning.
    • History of machine learning,
    • Types of problems and tasks in machine learning and its algorithms.
  • Perceptron learning and Neural networks
    • What is a learning rule?
    • How to develop the perceptron learning rule?
    • Advantages and disadvantages of the perceptron rule.
    • The model of perceptron learning with theory and examples.
  • The types of neural networks
    • single layer perceptron network and multi-layer neuron network
    • The perceptron network architecture
    • Steps for constructing learning rules
    • Linear separable problem
    • Back propagation algorithm and learning rule in multi-layer perceptron
    • How to calculate back propagation algorithm
  • Updation of weight
    • The weight matrix of perceptron.
    • Learning of processing elements related to weight.
  • Clustering algorithms
    • Modeling approaches: Centroid-based.
    • Modeling approaches: Hierarchical.
    • Class of problem.
    • Class of methods.
    • Cluster algorithm: k-Means
    • Cluster algorithm: k-Medians
    • Cluster algorithm: Expectation Maximisation
    • Cluster algorithm: Hierarchical clustering
Learning Objectives:
  • Logic reasoning overview
    • Facts about logics in artificial intelligence.
    • Why it is useful?
    • The arguments and its logical meanings.
    • Proof theory.
    • Theorems, semantics, models and arguments.
  • First Order Predicate calculus (FOPC)
    • Predicate calculus: Variables and Constants.
    • Formula for FOPC
  • Modus ponens and Modus tollens
    • Conditional statement and the affirmation of the antecedent of the conditional statement.
  • Unification and deduction process
    • Unification algorithm.
    • Expressions and transactions.
  • Resolution refutation
    • Resolution rules - meaning, propositional and example
    • Power of false and other examples
  • Skolemization
    • what is Skolemization?
    • How Skolemization works?
    • Uses of Skolemization
    • Skolem theories
Learning Objectives:
  • Production system
    • What is production system?
    • Components of AI production system.
    • Four classes of production system.
    • Advantages and disadvantages of production system.
    • Rules and commands of production system.
    • Data driven search.
    • Goal driven search.
    • Its differences.
  • CLIPS installation and CLISP Training/Tutorial (ai programing language)
    • What is CLIPS?
    • What are expert systems?
    • History of CLIPS
    • Facts and Rules
    • Components of CLIPS
    • Variables and Pattern matching
    • Defining classes and instances
    • Wildcard matching
    • Field constraints
    • Mathematical operators
    • Truth and control tutorial
Learning Objectives:
  • Intelligent agent
    • Generic agent.
    • Autonomous agent.
    • Reflex agent.
    • Goal based agent.
    • Utility based agent.
  • Utility theory
    • Utility functions.
    • Maximize expected utility.
    • Basis of utility theory.
    • Six axioms of utility theory.
  • Decision theory
    • Introduction to decision theory.
    • Perspectives and disciplines of decision science.
    • A few different decision theory also explained
  • Decision network
    • Graphical representation of a decision problem.
  • Reinforcement learning
    • why reinforcement learning?
    • How does it work?
    • What are the motivations?
    • What technology is used?
    • Who uses it?
    • Where can the reinforcement learning be applied?
    • The limitations of reinforcement learning.
  • Markov Decision Processes (MDP)
    • Objectives.
    • Functions.
    • Models.
    • Dynamic programming.
    • Linear programming.
  • Dynamic Decision Networks (DDN)
    • Features.
    • Representations.
    • Components.
    • DDN is a feature based extension of MDP.
Learning Objectives:
  • Basics of set theory
    • Importance of set theory.
    • What is a set.
    • Set notation.
    • Well defined sets.
    • Number sets.
    • Set equality.
    • Cardinality of a set.
    • Subsets and proper subsets and finally power sets
    • Basic concepts.
  • Probability distribution
    • Joint probability distribution.
  • Bayesian rule for conditional probability
    • What is Bayes’ theorem
    • How to calculate conditional probability using Bayes’ theorem?


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