AI & ML Course Syllabus
Introduction to AI and ML
- What is AI? History and milestones in AI
- Differences between AI, ML, and Data Science
- Types of AI: Narrow AI, General AI, Super AI
- Introduction to Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
Mathematical Foundations of Machine Learning
- Linear Algebra for Machine Learning: Vectors, Matrices, Tensors
- Probability and Statistics: Bayes Theorem, Conditional Probability, Probability Distributions
- Optimization Techniques: Gradient Descent, Stochastic Gradient Descent
Supervised Learning - Regression
- Linear Regression
- Polynomial Regression
- Logistic Regression
- Evaluation Metrics: MSE, RMSE, MAE, R-Squared
- Implementation using Scikit-learn
Supervised Learning - Classification
- K-Nearest Neighbors (KNN)
- Decision Trees
- Random Forests
- Support Vector Machines (SVM)
- Evaluation Metrics: Accuracy, Precision, Recall, F1 Score
- Hands-on with a classification problem
Unsupervised Learning
- Introduction to Clustering
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis (PCA) and Dimensionality Reduction
- Use cases and applications
Neural Networks and Deep Learning Basics
- Introduction to Neural Networks: Perceptrons, Activation Functions
- Backpropagation and Gradient Descent in Neural Networks
- Deep Learning: Concepts and Need for Deep Networks
- Popular Deep Learning Frameworks (TensorFlow, Keras, PyTorch)
- Implementation of basic Neural Network
Convolutional Neural Networks (CNN)
- CNN Architecture: Convolutional Layers, Pooling Layers, Fully Connected Layers
- Image Recognition and Object Detection with CNNs
- Popular CNN Architectures (AlexNet, VGGNet, ResNet)
- Hands-on with CNN for image classification
Recurrent Neural Networks (RNN) and LSTMs
- Sequential Data and RNNs
- Long Short-Term Memory (LSTM) Networks
- Applications of RNNs in Natural Language Processing (NLP)
- Sentiment Analysis, Text Generation
- Practical implementation of RNN/LSTM
Reinforcement Learning
- Basics of Reinforcement Learning: Markov Decision Process
- Exploration vs Exploitation, Reward Maximization
- Q-Learning and Deep Q-Networks (DQN)
- Applications of RL: Game AI, Robotics
- Implementation of a simple RL agent
Advanced Topics in AI
- Transfer Learning
- Generative Adversarial Networks (GANs)
- Autoencoders
- AI in Healthcare, Autonomous Systems, and Finance
AI Ethics and Fairness
- Ethical Challenges in AI
- Bias in Machine Learning Algorithms
- Fairness, Transparency, and Accountability
- Legal and societal impacts of AI
AI and ML Project Work
- Project: Implementing a real-world AI or ML model
- Model Evaluation and Tuning (Cross-validation, Grid Search, Random Search)
- Final Model Presentation and Evaluation
AI & ML Syllabus
1. Introduction to AI and ML
2. Mathematical Foundations of Machine Learning
3. Supervised Learning - Regression
4. Supervised Learning - Classification
5. Unsupervised Learning
6. Neural Networks and Deep Learning Basics
7. Convolutional Neural Networks (CNN)
8. Recurrent Neural Networks (RNN) and LSTMs
9. Reinforcement Learning
10. Advanced Topics in AI
11. AI Ethics and Fairness
12. AI and ML Project Work
