Artificial Intelligence Training and Internship

1. Introduction to Artificial Intelligence (Beginner Level)

Objective: Provide students with a broad overview of AI, its history, and its fundamental principles.

Topics:

• Introduction to AI
  • Definition of AI
  • Brief history of AI
  • Applications of AI in various domains (healthcare, finance, robotics, etc.)
  • Types of AI: Narrow AI vs. General AI
  • AI vs. Machine Learning vs. Deep Learning
• Foundational Concepts in AI
  • Problem-solving and search algorithms
  • State space representation
  • Informed vs. uninformed search
  • Heuristics
• AI Ethics and Societal Impacts
  • Bias in AI models
  • AI ethics and fairness
  • Safety and transparency in AI systems
  • The future of AI and job displacement

Hands-on:

• Introduction to Python for AI
• Simple AI algorithms (e.g., Tic-Tac-Toe, 8-Puzzle)

2. Introduction to Machine Learning (Beginner to Intermediate Level)

Objective: Introduce machine learning, its types, and basic algorithms used in AI systems.

Topics:

• Overview of Machine Learning
  • Supervised Learning, Unsupervised Learning, Reinforcement Learning
  • The role of data in ML
• Data Pre-processing
  • Data cleaning
  • Data normalization and scaling
  • Handling missing values
  • Feature engineering
• Supervised Learning Algorithms
  • Linear Regression
  • Logistic Regression
  • K-Nearest Neighbour's (K-NN)
  • Support Vector Machines (SVM)
  • Decision Trees and Random Forests
  • Neural Networks (Basic introduction)
• Evaluation Metrics
  • Accuracy, Precision, Recall, F1-Score
  • Confusion Matrix
  • Cross-validation

Hands-on:

• Implementing basic machine learning algorithms in Python (using scikit-learn)
• Building models for real-world datasets (e.g., Titanic dataset, Iris dataset)

3. Deep Learning Fundamentals (Intermediate Level)

Objective: Dive into deep learning techniques and neural networks, which form the core of modern AI.

Topics:

• Introduction to Neural Networks
  • Perceptron model
  • Multilayer perceptron's (MLPs)
  • Backpropagation algorithm
  • Gradient Descent
• Deep Learning Architectures
  • Convolutional Neural Networks (CNNs) for image recognition
  • Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for sequential data
  • Autoencoders for unsupervised learning
• Advanced Topics in Deep Learning
  • Generative Adversarial Networks (GANs)
  • Transfer Learning
  • Attention Mechanisms (e.g., Transformer models)
  • Reinforcement Learning and Q-learning

Hands-on:

• Building a simple neural network from scratch (using TensorFlow or PyTorch)
• Implementing CNNs for image classification
• Implementing RNNs/LSTMs for text generation or sentiment analysis

4. Natural Language Processing (Intermediate to Advanced Level)

Objective: Explore techniques and models for working with natural language data, a key AI application.

Topics:

• Introduction to NLP
  • Text reprocessing (tokenization, stemming, lemmatization)
  • Bag of Words and TF-IDF models
• Language Models
  • N-grams and Markov Chains
  • Word Embeddings (Word2Vec, Glove)
  • Recurrent models for text (RNNs, LSTMs)
• Transformer-based Models
  • Introduction to Attention Mechanism
  • BERT, GPT, and other pre-trained transformer models
  • Fine-tuning transformer models for specific tasks (text classification, named entity recognition)
• Applications of NLP
  • Sentiment analysis
  • Named Entity Recognition (NER)
  • Machine Translation
  • Question Answering Systems

Hands-on:

• Implementing a chatbot using Seq2Seq models
• Fine-tuning BERT for text classification
• Building a sentiment analysis model using pre-trained embeddings

5. Reinforcement Learning (Advanced Level)

Objective: Introduce reinforcement learning (RL), where agents learn through interaction with the environment.

Topics:

• Foundations of Reinforcement Learning
  • Agents, environment, and rewards
  • Markov Decision Processes (MDPs)
  • Policies, value functions, and Q-values
  • Bellman equations
• Key RL Algorithms
  • Monte Carlo methods
  • Temporal Difference (TD) learning
  • Q-learning and Deep Q-Networks (DQN)
• Advanced RL Techniques
  • Policy Gradient methods
  • Actor-Critic models
  • Proximal Policy Optimization (PPO)
• Applications of RL
  • Robotics
  • Game-playing AI (e.g., AlphaGo, Open AI Gym)
  • Autonomous driving

Hands-on:

• Building a Q-learning agent to play simple games
• Implementing a DQN for Atari games
• Reinforcement learning in simulated environments (e.g., Open AI Gym)

6. Computer Vision (Advanced Level)

Objective: Focus on image processing and computer vision tasks using AI techniques.

Topics:

• Introduction to Computer Vision
  • Image processing basics (filters, edge detection)
  • Feature extraction (SIFT, HOG, etc.)
  • Convolutional Neural Networks (CNNs) for image recognition
• Advanced Vision Techniques
  • Object detection (e.g., YOLO, Faster R-CNN)
  • Semantic segmentation (e.g., U-Net)
  • Image generation and style transfer using GANs
• Applications of Computer Vision
  • Face recognition
  • Image captioning
  • Optical Character Recognition (OCR)
  • Autonomous vehicles and object tracking

Hands-on:

• Building an image classifier using CNNs
• Implementing object detection with pre-trained models (e.g., YOLO)
• Generating new images using GANs

7. AI System Deployment and Best Practices (Advanced Level)

Objective: Teach students how to deploy AI models into production environments and follow best practices in AI development.

Topics:

• Model Deployment
  • Model serialization (saving/loading models)
  • Cloud-based deployment (AWS, Google Cloud, Azure)
  • Containerization (Docker) and model APIs (Flask/Fast API)
• Scalability and Efficiency
  • Model optimization for inference (quantization, pruning)
  • Distributed computing (Hadoop, Spark)
  • GPU vs CPU considerations for AI
• Monitoring and Maintenance
  • Monitoring model performance over time
  • Model retraining and updating
  • Handling model drift

Hands-on:

• Deploying an AI model as a web service (Flask/Fast API)
• Model optimization for faster inference
• Setting up a cloud-based pipeline for model deployment

8. Capstone Project

Objective: Allow students to apply the knowledge they have gained throughout the course to a real-world AI project.

Topics:

• Students select a problem from areas such as NLP, computer vision, robotics, or reinforcement learning.
• Work in teams or individually to design, implement, and deploy an AI solution.
• The project should involve data collection, model training, evaluation, and deployment.

Expected Deliverables:

• A working AI model
• Documentation and codebase
• Presentation of the project, explaining the methodology, challenges, and results

Course Resources:

• Recommended Textbooks:
  • Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
  • Deep Learning by Ian Good fellow, Yoshua Bengio, and Aaron Courville
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
  •  
• Online Platforms:
  • Coursera, edX, Udacity (AI/ML/DL courses)
  • GitHub repositories for hands-on examples