Artificial Intelligence 6-week

The Artificial Intelligence (AI) course is a structured 6-week program designed to provide a strong foundation in the core concepts, techniques, and real-world applications of AI. This course combines theoretical understanding with practical implementation to help learners build intelligent systems and solve complex problems.

📅 Course Duration: 6 Weeks

🎯 Level: Beginner to Intermediate

Week 1: Introduction to Artificial Intelligence

What is Artificial Intelligence?
History and evolution of AI
Types of AI (Narrow AI, General AI, Super AI)
Applications of AI in real-world scenarios
Overview of intelligent agents

Week 2: Problem Solving & Search Techniques

State space representation
Uninformed search (BFS, DFS, Uniform Cost Search)
Informed search (Greedy Search, A* Algorithm)
Heuristics and optimization
Game playing strategies (Minimax, Alpha-Beta Pruning)

Week 3: Knowledge Representation & Reasoning

Propositional logic
First-Order Logic (FOL)
Inference mechanisms
Resolution and theorem proving
Rule-based systems

Week 4: Planning & Intelligent Agents

Types of agents
Planning problems
Partial Order Planning
Decision making in AI systems
Real-world AI architectures

Week 5: Machine Learning Fundamentals

Introduction to Machine Learning
Supervised vs Unsupervised learning
Classification and Regression
Basic algorithms (Linear Regression, KNN, Decision Trees)
Model evaluation techniques

Week 6: Uncertainty & Modern AI Applications

Probability basics
Bayesian reasoning
Bayesian Networks
Introduction to Neural Networks
AI ethics and future trends

🚀 Learning Outcomes

By the end of this 6-week course, learners will be able to:

Understand core AI concepts and terminology
Apply search and problem-solving algorithms
Represent knowledge using logical systems
Implement basic machine learning models
Analyze AI systems under uncertainty
Build simple AI-based applications

Course Content

Week 1
Week 1 lays the foundation for understanding Artificial Intelligence (AI) by introducing its core concepts, history, and real-world significance. Students begin by exploring what AI is, how it evolved over time, and why it has become one of the most transformative technologies in modern computing. The week covers the different types of AI, including Narrow AI, General AI, and Super AI, helping learners understand the current capabilities and future possibilities of intelligent systems. Key application areas such as healthcare, finance, robotics, education, and autonomous systems are also discussed to demonstrate AI’s real-world impact. Students are introduced to the concept of intelligent agents, including how agents perceive their environment, make decisions, and take actions to achieve specific goals. Basic terminology such as rationality, environment types, and performance measures is explained to build a strong theoretical base. By the end of Week 1, learners gain: A clear understanding of what Artificial Intelligence is Knowledge of AI history and major milestones Insight into real-world AI applications A foundational understanding of intelligent agents and their components This week builds the essential groundwork needed to move into problem-solving techniques and advanced AI concepts in the upcoming weeks.

Lesson 1

08:30
Lesson 2

10:47
Quiz on week 1

Week 2
Week 2 focuses on one of the core areas of Artificial Intelligence — problem solving using search algorithms. This week builds the computational foundation required to design intelligent systems that can find solutions in complex environments. Students begin by understanding how problems are formally defined in AI, including: Initial state Goal state State space Actions Path cost This representation helps transform real-world problems into mathematical models that AI systems can solve. Uninformed (Blind) Search Techniques Learners study search strategies that do not use additional knowledge about the goal: Breadth-First Search (BFS) Depth-First Search (DFS) Uniform Cost Search (UCS) These methods systematically explore the state space but may not always be efficient. Informed (Heuristic) Search Techniques The week then introduces heuristics, which guide the search process toward the goal more efficiently. Key algorithms include: Greedy Best-First Search A* (A-Star) Algorithm Students learn how heuristic functions improve performance and reduce computational cost. Game Playing in AI Week 2 also covers AI decision-making in competitive environments through: Minimax Algorithm Alpha-Beta Pruning These techniques are used in strategic games like chess and tic-tac-toe to make optimal decisions. Learning Outcomes of Week 2 By the end of this week, students are able to: Formulate problems in AI Apply uninformed search algorithms Implement heuristic-based search methods Understand optimality and completeness Analyze time and space complexity of algorithms Apply game-playing strategies Week 2 provides essential algorithmic tools that enable AI systems to reason, plan, and make intelligent decisions efficiently. This forms the backbone for advanced topics like knowledge representation and reasoning in the upcoming weeks.

Week 3
Week 3 focuses on how Artificial Intelligence systems represent knowledge and perform logical reasoning. After learning search techniques in Week 2, students now explore how AI systems store information and draw conclusions intelligently. This week introduces Propositional Logic and First-Order Logic (FOL) as formal methods to represent facts and relationships. Students learn how to construct logical statements, use logical connectives, and apply inference rules to derive new knowledge. Key topics include: Syntax and semantics of propositional logic Truth tables and logical equivalence Inference rules (Modus Ponens, Modus Tollens) First-Order Logic (predicates, quantifiers, variables) Unification and resolution Theorem proving Students also understand how reasoning systems help AI make decisions based on available knowledge. Learning Outcomes: By the end of Week 3, students can: Represent knowledge using logical systems Apply inference techniques Convert statements into logical form Perform resolution-based reasoning Week 3 builds the logical foundation required for advanced AI topics like planning and reasoning under uncertainty in the upcoming weeks.

Week 4
Week 4 focuses on Planning and Intelligent Agents, where AI systems move beyond simple search and logical reasoning to structured goal-based action planning. This week introduces the concept of planning problems, where an agent must determine a sequence of actions that leads from an initial state to a goal state. Students learn how AI systems create structured plans rather than simply reacting to the environment. Key Topics Covered: Planning Problem Formulation Defining initial state, goal state, actions, and constraints. State-Space Planning Forward (progression) and backward (regression) planning approaches. Partial Order Planning (POP) A flexible planning method that does not strictly fix action order unless required. Goal-Based Agents Agents that act specifically to achieve defined goals. Decision Making in AI How intelligent systems choose optimal actions based on available knowledge. Learning Outcomes: By the end of Week 4, students are able to: Understand how planning differs from simple search Formulate planning problems correctly Explain forward and backward planning Understand Partial Order Planning Analyze how goal-based agents operate Design basic AI planning strategies Week 4 strengthens students’ understanding of how intelligent systems plan actions systematically to achieve goals. This prepares learners for Week 5, where uncertainty and probabilistic reasoning are introduced.

Week 5
Week 5 introduces Machine Learning (ML), a major branch of Artificial Intelligence that enables systems to learn from data and improve their performance without being explicitly programmed. This week focuses on the core concepts of learning from examples, identifying patterns, and making predictions. Students explore different types of learning: Supervised Learning (classification and regression) Unsupervised Learning (clustering and pattern discovery) Key algorithms such as: Linear Regression K-Nearest Neighbors (KNN) Decision Trees are introduced to help students understand how machines make predictions and decisions based on data. Students also learn about: Training and testing datasets Model evaluation techniques Accuracy, precision, and error measurement Learning Outcomes: By the end of Week 5, students can: Understand the basics of Machine Learning Differentiate between supervised and unsupervised learning Apply simple ML algorithms Evaluate model performance Week 5 shifts the focus from rule-based intelligence to data-driven learning, preparing students for advanced topics like uncertainty and probabilistic reasoning in Week 6.

Week 6
Week 6, the final week of the course, focuses on how AI systems make decisions under uncertainty. In real-world situations, information is often incomplete or uncertain, so AI must rely on probability-based reasoning instead of fixed logic. This week introduces: Basic Probability Concepts Conditional Probability & Bayes’ Theorem Bayesian Networks Reasoning under Uncertainty Introduction to Neural Networks Ethical considerations in AI Students learn how probabilistic models help AI systems handle risk, incomplete data, and uncertain environments. Bayesian reasoning allows machines to update beliefs when new evidence is available. Learning Outcomes: By the end of Week 6, students can: Apply probability in AI reasoning Understand Bayesian inference Explain Bayesian Networks Recognize the importance of ethical AI Week 6 concludes the course by combining logic, learning, and probability — giving students a complete foundational understanding of Artificial Intelligence.

A course by

Kushawaha Shankar

CAD CAM developer.

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Price:
Original price was: ₹700.00.Current price is: ₹400.00.
Instructor:Kushawaha Shankar
Duration: 20 hours
Lessons:12
Students:4
Level:All Levels