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artificial intelligence

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About Specialization:

Artificial Intelligence

AI has applications across various industries, including healthcare, finance, transportation, manufacturing, and entertainment. It’s continually evolving and advancing, driven by research breakthroughs, technological innovations, and the increasing availability of data. However, ethical considerations surrounding AI, such as bias in algorithms, data privacy, and job displacement, are also important areas of concern that researchers and policymakers are actively addressing. AI systems are designed to analyze large amounts of data, recognize patterns within that data, and use those patterns to make predictions or decisions.

Key Features/Course content

Videos

 

  1. Introduction to AI
  2. Intelligent Agents
  3. Problem Solving by Search:
    1. Problem-Solving Agents
    2. Searching for Solutions
    3. Uninformed Search Strategies:
      1. Breadth-first search
      2. Uniform cost search
      3. Depth-first search
      4. Iterative deepening Depth-first search
      5. Bidirectional search
  4. Informed (Heuristic) SearchStrategies:
    1. Greedy best-first search,
    2. A* search
    3. Heuristic Functions
  5. Beyond Classical Search:
    1. Hill-climbing search
    2. Simulated annealing search
    3. Local Search in Continuous Spaces
    4. Searching with Non-Deterministic Actions
    5. Searching with Partial Observations
    6. Online Search Agents and Unknown Environment
  1. Adversarial Search Games:
    1. Game Theory
    2. Optimal Decisions in Games
    3. Alpha–Beta Pruning
    4. Imperfect Real-Time Decisions
  2. Constraint Satisfaction Problems:
    1. Defining Constraint Satisfaction Problems
    2. Constraint Propagation
    3. Backtracking Search for CSPs
    4. Local Search for CSPs
    5. The Structure of Problems
  3. Propositional Logic:
    1. Knowledge-Based Agents
    2. The Wumpus World Logic
    3. Propositional Logic
  4. Propositional Theorem Proving:
    1. Inference and proofs
    2. Proof by resolution
    3. Horn clauses and definite clauses
    4. Forward and backward chaining
    5. Effective Propositional Model Checking
    6. Agents Based on Propositional Logic
  1. First-Order Logic:
    1. Representation
    2. Syntax and Semantics of First-Order Logic
    3. Using FirstOrder Logic
    4. Knowledge Engineering in First-Order Logic
  2. Inference in First-Order Logic:
    1. Propositional vs. First-Order Inference
    2. Unification and Lifting
    3. Forward Chaining
    4. Backward Chaining
    5. Resolution
  3. Knowledge Representation:
    1. Ontological Engineering
    2. Categories and Objects
    3. Events
    4. Mental Objects
    5. Reasoning Systems for Categories
    6. Reasoning with Default Information
  1. Classical Planning:
    1. Definition of Classical Planning
    2. Algorithms for Planning with StateSpace Search
    3. Planning Graphs
    4. Other Classical Planning Approaches
    5. Analysis of Planning approaches
    6. Planning and Acting in the Real World:
      1. Time, Schedules, and Resources
      2. Hierarchical Planning
      3. Planning and Acting in Non-deterministic Domains
      4. Multi agent Planning
  1. Uncertainty:
    1. Acting under Uncertainty
    2. Basic Probability Notation
    3. Inference Using Full Joint Distributions
    4. Independence
    5. Bayes’ Rule and Its Use
  2. Probabilistic Reasoning:
  3. Representing Knowledge in an Uncertain Domain
  4. The Semantics of Bayesian Networks
  5. Efficient Representation of Conditional Distributions
  6. Approximate Inference in Bayesian Networks
  7. Relational and First-Order Probability
  8. Other Approaches to Uncertain Reasoning
  9. Dempster-Shafer theory
  10. Learning:
    1. Forms of Learning
    2. Supervised Learning
    3. Learning Decision Trees
  11. Knowledge in Learning:
    1. Logical Formulation of Learning
    2. Knowledge in Learning
    3. Explanation-Based Learning
    4. Learning Using Relevance Information
    5. Inductive Logic Programming
  1. Artificial Intelligence A Modern Approach, Third Edition, Stuart Russell and Peter Norvig, Pearson Education
  2. Artificial Intelligence, 3rd Edn., E. Rich and K. Knight (TMH)
  3. Artificial Intelligence, Saroj Kaushik
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