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.
Section 1: Overview of Artificial Intelligence
Introduction to Artificial Intelligence
Artificial Intelligence is a branch of science which makes machines to solve the complex problems in a human way. This chapter contains a history of artificial intelligence, detailed explanation of Artificial intelligence with a definition and meaning. It also explains why artificial intelligence is important in today’s world, what is involved in artificial intelligence and the academic disciplines which are related to artificial intelligence.
This section will help you to learn what is intelligent agents, agents, and environment, a concept of rationality, types of agents – Generic agent, Autonomous agent, Reflex agent, Goal-Based Agent, Utility-based agent. The basis of classification of the agents is also explained in detail. The types of environment are also explained with examples.
Section 2: Representation and Search: State Space Search
Information on State Space Search
This chapter gives a brief introduction to State Space Search in artificial intelligence, its representation, components of search systems and the areas where state space search is used.
Graph theory on state space search
Under this chapter, you will learn what is a graph theory and how it may be used to model problem solving as a search through a graph of problem states. The And/or graph is explained with its uses. The components of the graph theory are also given a brief introduction.
Problem-Solving through state space search
The topics included in this section includes General Problem, Variants, types of problem-solving approach is explained with examples.
Depth First Search searches deeper into the problem space. This section also includes the advantages, disadvantages, and algorithm of depth-first search.
DFS with iterative deepening (DFID)
This is a combination of breadth-first search and depth-first search. In this section, you will learn what is an iterative deepening search, its properties, and algorithm along with examples.
Backtracking is an implementation of Artificial Intelligence. This section explains what is backtracking, description of the method when backtracking can be used and for what applications backtracking algorithm can be used. It is explained with a few examples and graphs.
Section 3: Representation and Search: Heuristic Search
Heuristic search overview
Heuristic search is a search technique that employs a rule of thumb for its moves. It plays a major role in search strategies. In this chapter, the general meaning and the technical meaning of Heuristic search is explained. It contains more information about the Heuristic search along with the function of the nodes and the goals. The section also contains the following topics which are its type of techniques
Pure Heuristic Search
Iterative- Deepening A*
Depth First Branch and Bound
Heuristic Path Algorithm
Recursive Best-First Search
Simple hill climbing
This chapter explains the Simple Hill Climbing technique in Heuristic search, function optimization of hill climbing, problems with simple hill climbing and its example.
Best first search algorithm
This algorithm combines the advantages of breadth first and depth first searches. This algorithm finds the most promising path. It is explained with examples.
This algorithm is used to estimate the cost to reach the goal state. In this chapter, you will learn what is admissibility heuristic, its formulation, construction and examples of admissible heuristic using a puzzle problem.
Min Max algorithm
This algorithm is used in two-player games such as Chess and others. This section involves a brief introduction to search trees, introduction to the algorithm, explanation of the two players MIN and MAX, optimization, speeding the algorithm, adding alpha beta cut-offs and an example using a game is given for your easy understanding.
Alpha beta pruning
Alpha beta pruning is a method to reduce the number of nodes in the minimax algorithm in its search tree. This chapter explains the Alpha value of the node, Beta value of the node, improvements over the minimax algorithm, its Pseudo code and a detailed game example.
Section 5: Logic and Reasoning
Logic reasoning overview
Logic is the study of what follows from what. This section explains the facts about logics in artificial intelligence, why it is useful, the arguments and its logical meanings are explained in detail. Proof theory is used to check the validity of the arguments.
In propositional logic lexicon and grammar are the syntax used and it is explained in detail under this topic along with the symbols used. The theorems, semantics, models and arguments are also mentioned in this chapter.
First Order Predicate calculus (FOPC)
FOPC includes a wide range of entities. The predicate calculus includes variables and constants. The formula for FOPC is defined and each of its symbol is explained in detail with examples.
Modus ponens and Modus tollens
Modus Ponens and Modus tollens are forms of valid inferences. Modus Ponens involves two premises – conditional statement and the affirmation of the antecedent of the conditional statement. Both the terms are explained with examples.
Unification and deduction process
The unification algorithm, its expressions and transactions are given in this chapter
Resolution rules, its meaning, propositional resolution example, power of false and other examples are given in brief in this section.
This chapter explains what is Skolemization, how it works, uses of Skolemization and Skolem theories in detail.
Section 6: Rule Based Programming
This section contains what is production system, components of AI production system, four classes of production system, advantages and disadvantages of production system. It also contains the following topics
Rules and commands of production system
Data driven search
Goal driven search
CLIPS installation and clips tutorial
The topics included in this section are listed below
What is CLIPS ?
What are expert systems ?
History of CLIPS
Facts and Rules
Components of CLIPS
Variables and Pattern matching
Defining classes and instances
Truth and control tutorial
Section 7: Decision Making
This section starts with an brief introduction to intelligent agent. The different types of agents are covered in this topic as mentioned in the list below
Goal based agent
Utility based agent
All these types of agents are explained with a pictorial representation and example.
This section covers the following topics
Maximize expected utility
Basis of utility theory
Six axioms of utility theory
This chapter gives a brief introduction to decision theory, its perspectives and disciplines of decision science. The different decision theory is also explained in detail.
Decision network is a graphical representation of a decision problem. It is discussed in this chapter in detail with examples.
This includes a definition, 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 and the limitations of reinforcement learning.
Markov Decision Processes (MDP)
This section includes the objectives, functions, models, dynamic programming, linear programming and examples.
Dynamic Decision Networks (DDN)
DDN is a feature based extension of MDP. This section explains its features, representations, components along with examples.
Section 8: Stochastic methods
Basics of set theory
Here you will learn the 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. It also includes the basic concepts in set theory.
The joint probability distribution is explained in this section with an example and pictorial representation.
Bayesian rule for conditional probability
This section explains what is Bayes’ theorem and how to calculate conditional probability using Bayes’ theorem. This is explained with few illustrations of college life, medical diagnosis and witness reliability.
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.
On successful completion of the training program, participants will receive the certificate of participation.