Course curriculum

  • 1
    Introduction to the course
  • 2
    Introduction to AI
  • 3
    Problem Solving
  • 4
    Knowledge Representation And Planning
  • 5
    Probabilistic Reasoning
  • 6
    Machine Learning
  • 7
    Communication and Perceiving
  • 8
    Neural Network
  • 9
    Data Mining Tools
  • 10
    Projects
  • 11
    Study Material
  • Introduction to the course
  • What is Artificial Intelligence? Intelligent Agents Advantages and Disadvantages of AI Challenges of AI
  • What is Searching? Uninformed Search Algorithm Informed Search Algorithm Adversarial Search Constraint satisfaction problems
  • Knowledge Representation Knowledge Representation Techniques Propositional Logic First Order Logic Rule-Based System
  • Basic Probability Concepts Markov and Hidden Markov Model Association rules Dimensionality reduction Feature selection and Feature Extraction
  • What is Machine Learning? Types of Learning Clustering Classification Decision Tree Regression Support Vector Machine What is Reinforcement learning?
  • Natural Language Processing Perception
  • What is a Neural Network? Types of Neural Network Neural Network Components
  • RapidMiner Working with RapidMiner Weka Working with Weka Orange R/RStudio KNIME
  • Installing Prerequisites Clustering using K-means in Python
  • Slide Deck