1 Introduction to AI

·      What is AI?

·      History and Evolution of AI

·      Applications of AI in Various Domains

·      Ethical and Societal Considerations

2 Problem Solving and Search Algorithms

·      State Space Search

·      Uninformed Search Algorithms (e.g., BFS, DFS)

·      Informed Search Algorithms (e.g., A*)

·      Heuristic Functions

·      Local Search (e.g., Hill Climbing, Simulated Annealing)

3 Machine Learning Basics

·      Introduction to machine learning and its types

·      Supervised, unsupervised, and reinforcement learning

·      Data preprocessing and feature engineering

4 Supervised Learning Algorithms

·      Linear regression

·      Logistic regression

·      Decision trees

·      Random forests

·      Support vector machines (SVM)

·      Evaluation metrics for classification and regression

5 Unsupervised Learning Algorithms

·      Clustering algorithms: K-means, hierarchical clustering, DBSCAN

·      Dimensionality reduction: PCA, LDA

·      Association rule mining

6 Neural Networks and Deep Learning

·      Introduction to artificial neural networks (ANNs)

·      Feedforward neural networks

·      Convolutional neural networks (CNNs)

·      Recurrent neural networks (RNNs)

·      Training techniques: Backpropagation, optimization algorithms

·      Deep learning frameworks: TensorFlow, PyTorch

7 Natural Language Processing (NLP)

·      Text preprocessing and tokenization

·      Word embeddings: Word2Vec, GloVe

·      NLP tasks: Sentiment analysis, named entity recognition, language translation

·      Sequence-to-sequence models

·      Attention mechanisms

8 Computer Vision

·      Image preprocessing

·      Convolutional neural networks for image classification and object detection

·      Transfer learning in computer vision

·      Applications of computer vision: Face recognition, image generation

9 Reinforcement Learning

·      Basics of reinforcement learning

·      Markov decision processes (MDPs)

·      Q-learning and Deep Q Networks (DQNs)

·      Policy-based methods and actor-critic methods

10 Advanced AI Topics

·      Generative Adversarial Networks (GANs)

·      Transfer learning and domain adaptation

·      Explainable AI

·      AI in robotics and autonomous systems

11: Ethical and Social Implications of AI

·      Bias and fairness in AI

·      AI and privacy

·      AI and jobs: Automation and societal impact

·      Responsible AI development and deployment