Basics of AI

About This Course

Aim

Program Objectives

Program Structure

Module 1: Introduction to Artificial Intelligence

1. Overview of AI
   • Definition, History, and Evolution of AI
   • Key Concepts: Intelligence, Machine Learning, and Automation
   • AI vs. Human Intelligence
   • Applications of AI in Various Domains
2. AI Foundations
   • Philosophical and Ethical Implications
   • Key Milestones in AI Development
   • Types of AI: Narrow AI, General AI, and Superintelligent AI
3. AI Technologies and Tools
   • AI Ecosystem and Frameworks
   • Popular Programming Languages for AI (Python, R, etc.)
   • Overview of AI Libraries (TensorFlow, PyTorch, etc.)

Module 2: Machine Learning

1. Introduction to Machine Learning
   • Definitions and Types (Supervised, Unsupervised, Reinforcement Learning)
   • Key Algorithms and Techniques
2. Supervised Learning
   • Regression and Classification Techniques
   • Evaluation Metrics (Accuracy, Precision, Recall, F1 Score)
3. Unsupervised Learning
   • Clustering Algorithms (K-means, DBSCAN)
   • Dimensionality Reduction (PCA, t-SNE)
4. Reinforcement Learning
   • Concepts of Reward Systems
   • Deep Q-Learning and Policy Optimization

Module 3: Deep Learning

1. Foundations of Deep Learning
   • Neural Networks: Structure and Functioning
   • Activation Functions, Loss Functions
2. Deep Learning Architectures
   • Convolutional Neural Networks (CNNs)
   • Recurrent Neural Networks (RNNs)
   • Generative Adversarial Networks (GANs)
3. Training and Optimization
   • Backpropagation and Gradient Descent
   • Hyperparameter Tuning and Model Regularization

Module 4: Natural Language Processing (NLP)

1. Introduction to NLP
   • Text Preprocessing and Representation
   • N-grams, Bag of Words, TF-IDF
2. Core NLP Techniques
   • Sentiment Analysis, Named Entity Recognition
   • Machine Translation and Text Summarization
3. Transformers and Large Language Models
   • Overview of Transformers (BERT, GPT)
   • Applications of Large Language Models in Industry

Module 5: Computer Vision

1. Introduction to Computer Vision
   • Fundamentals of Image Processing
   • Object Detection and Recognition
2. Advanced Techniques in Vision
   • Image Segmentation (U-Net, Mask R-CNN)
   • Applications in Healthcare, Automotive, and Security

Module 6: AI in Practice

1. AI Applications in Industry
   • Use Cases in Healthcare, Finance, Retail, and Robotics
   • Smart Cities, Autonomous Vehicles
2. AI in Research and Development
   • Emerging Trends in AI Research
   • AI in Scientific Discovery
3. AI Ethics and Governance
   • Responsible AI and Ethical Implications
   • Regulatory Frameworks and AI Policies

Module 7: Advanced Topics in AI

1. Explainable AI (XAI)
   • Importance and Challenges of Interpretability
   • Techniques for Building Transparent AI Models
2. Federated Learning
   • Decentralized Machine Learning Models
   • Applications in Privacy-Sensitive Domains
3. AI for Social Good
   • AI in Environmental Sustainability
   • Applications in Education and Public Health

Module 8: Future Directions in AI

1. AI Trends and Technologies
   • Advances in Quantum AI
   • Neuromorphic Computing
2. AI Research Challenges
   • Open Problems in AI Development
   • Cross-Disciplinary Research Opportunities
3. AI for the Next Decade
   • Speculations on Superintelligence
   • The Role of AI in Shaping Future Societies

Who Should Enrol?

Program Outcomes

Fee Structure

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What You’ll Gain