Introduction to AI

• Definition, history, and scope of AI
• Types of AI (Narrow, General, Super AI).
• Applications of AI in different industries.
• Ethical and social implications of AI.

Foundations of AI

• Basic concepts in computer science and mathematics (linear algebra, probability, statistics)
• Problem-solving techniques in AI.
• Search algorithms (DFS, BFS, A*, Hill Climbing, Genetic Algorithms).
• Knowledge representation (logic, semantic networks, frames, ontologies).

• Introduction to ML (Supervised, Unsupervised, Reinforcement Learning)
• Regression and classification techniques
• Decision trees, Random Forests, SVMs, kNN, Naïve Bayes
• Clustering algorithms (k-Means, Hierarchical, DBSCAN)
• Evaluation metrics (accuracy, precision, recall, F1-score)

• Neural networks basics (perceptron, activation functions)
• Backpropagation and optimization algorithms
• Convolutional Neural Networks (CNNs) – image recognition
• Recurrent Neural Networks (RNNs, LSTMs, GRUs) – sequential data
• Generative Models (GANs, VAEs)

• Text preprocessing (tokenization, stemming, lemmatization)
• Language models (n-grams, word2vec, GloVe, BERT, GPT)
• Sentiment analysis and text classification
• Chatbots and conversational AI
• Speech recognition and machine translation

• Concepts of agent, environment, states, actions, rewards
• Markov Decision Processes (MDP)
• Q-Learning and Deep Q-Networks (DQN)
• Applications in robotics and game AI

• Programming languages for AI (Python, R, Julia)
• Libraries: NumPy, Pandas, Matplotlib
• ML/DL frameworks: Scikit-Learn, TensorFlow, PyTorch, Keras
• Cloud AI platforms (Google AI, AWS AI, Microsoft Azure AI)

• Computer Vision (object detection, face recognition, medical imaging)
• Autonomous systems (self-driving cars, drones)
• AI in IoT (Smart homes, smart cities)
• AI in business and decision support systems

• Responsible AI and fairness
• Bias in AI models and how to mitigate it
• AI governance and regulations
• Future trends: AGI, quantum AI, AI in space exploration

• Design and implement a real-world AI solution

Example projects:

• Chatbot for customer service
• Fake news detection system
• Image classifier for medical diagnosis
• Stock price prediction