Unit 1: Introduction to Artificial Intelligence

• What is Artificial Intelligence?

  • Definition and history of AI.
  • Goals and objectives of AI.
  • Importance and impact of AI on society and industry.

• Applications of AI

  • AI in healthcare, finance, robotics, gaming, and other industries.
  • AI in everyday life: virtual assistants, recommendation systems, autonomous vehicles.

• Branches of AI

  • Machine learning, natural language processing, robotics, expert systems, computer vision.

Unit 2: Problem Solving and Search Techniques

• Problem-Solving Strategies

  • Problem definition and formulation.
  • State space representation.
  • Search strategies: uninformed (blind) search, informed (heuristic) search.

• Search Algorithms

  • Uninformed search: BFS (Breadth-First Search), DFS (Depth-First Search), Uniform Cost Search.
  • Informed search: Greedy search, A* algorithm, Hill Climbing.
  • Constraint satisfaction problems and solutions.

Unit 3: Knowledge Representation and Reasoning

• Knowledge Representation Techniques

  • Logical representation: propositional logic, predicate logic.
  • Semantic networks, frames, scripts, ontologies.

• Reasoning Techniques

  • Deductive reasoning, inductive reasoning, abductive reasoning.
  • Resolution and unification in logic.
  • Forward chaining, backward chaining.

• Handling Uncertainty

  • Probabilistic reasoning: Bayesian networks.
  • Fuzzy logic and reasoning with uncertainty.

Unit 4: Machine Learning

• Introduction to Machine Learning

  • Definition and types of machine learning: supervised, unsupervised, reinforcement learning.
  • Applications of machine learning.

• Supervised Learning

  • Classification algorithms: Decision trees, k-nearest neighbors, Support Vector Machines (SVM), neural networks.
  • Regression algorithms: linear regression, logistic regression.

• Unsupervised Learning

  • Clustering algorithms: k-means, hierarchical clustering, DBSCAN.
  • Dimensionality reduction: PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis).

• Reinforcement Learning

  • Concepts of agents, environments, and rewards.
  • Q-learning, policy gradient methods.

Unit 5: Neural Networks and Deep Learning

• Introduction to Neural Networks

  • Structure and function of artificial neurons.
  • Types of neural networks: feedforward, recurrent, convolutional neural networks (CNNs).

• Deep Learning

  • Deep neural networks (DNNs) and backpropagation.
  • Convolutional neural networks (CNNs) for image processing.
  • Recurrent neural networks (RNNs) for sequence data.

• Training Deep Models

  • Gradient descent and optimization techniques.
  • Overfitting and regularization.
  • Transfer learning and fine-tuning.

Unit 6: Natural Language Processing (NLP)

• Introduction to NLP

  • Basics of NLP and its importance.
  • Applications of NLP: machine translation, sentiment analysis, chatbots.

• Text Processing Techniques

  • Tokenization, stemming, lemmatization.
  • Bag of words, TF-IDF (Term Frequency-Inverse Document Frequency).

• NLP Models and Algorithms

  • Language models: n-grams, word embeddings (Word2Vec, GloVe).
  • Sequence models: Hidden Markov Models (HMMs), Recurrent Neural Networks (RNNs), Transformer models (BERT, GPT).

Unit 7: Robotics and Computer Vision

• Introduction to Robotics

  • Components of a robotic system.
  • AI in robotics: perception, planning, action.

• Path Planning and Navigation

  • Pathfinding algorithms: A*, Dijkstra’s algorithm.
  • Robot localization and mapping.

• Computer Vision

  • Image processing and feature extraction.
  • Object detection and recognition.
  • Applications in autonomous vehicles, facial recognition, and surveillance.

Unit 8: Expert Systems and Fuzzy Logic

• Expert Systems

  • Definition and components: knowledge base, inference engine.
  • Rule-based systems and decision trees.
  • Applications of expert systems.

• Fuzzy Logic

  • Introduction to fuzzy sets and logic.
  • Fuzzy inference systems.
  • Applications of fuzzy logic in control systems and decision-making.

Unit 9: Ethics and Social Implications of AI

• Ethical Considerations

  • Bias and fairness in AI.
  • AI and privacy concerns.
  • Ethical AI development and deployment.

• Social Implications

  • Impact of AI on employment and the economy.
  • AI in decision-making: transparency and accountability.
  • The future of AI: opportunities and risks.

Unit 10: Current Trends and Future Directions in AI

• Recent Advances in AI

  • AI in healthcare: personalized medicine, medical imaging.
  • AI in finance: algorithmic trading, fraud detection.
  • AI in creative fields: music, art, and literature generation.

• Future Directions

  • General AI vs. Narrow AI.
  • AI and quantum computing.
  • The role of AI in solving global challenges.