About AI & ML Education

AI & ML Topics

Introduction to AI and ML

• Definition of Artificial Intelligence (AI) and Machine Learning (ML)
• Applications of AI and ML in various domains
• Difference between AI, ML, and Deep Learning

Linear Regression

• Simple linear regression
• Multiple linear regression
• Gradient descent algorithm
• Cost function and optimization

Classification

• Logistic Regression
• Decision Trees
• k-Nearest Neighbors (k-NN)
• Support Vector Machines (SVM)
• Naive Bayes Classifier

Clustering

• K-means clustering
• Hierarchical clustering
• Density-based clustering (DBSCAN)
• Evaluation metrics for clustering

Feature Engineering

• Handling missing data
• Feature scaling
• One-hot encoding
• Feature selection techniques

Evaluation Metrics

• Accuracy
• Precision, Recall, F1-Score
• Confusion Matrix
• ROC Curve and AUC-ROC

Overfitting and Underfitting

• Bias-variance tradeoff
• Regularization techniques (L1, L2 regularization)
• Cross-validation

Cross-Validation

• k-fold cross-validation
• Leave-One-Out cross-validation
• Stratified cross-validation

Bias-Variance Tradeoff

• Understanding bias and variance
• How to diagnose and address high bias/variance

Introduction to Neural Networks

• Perceptrons
• Activation functions (sigmoid, tanh, ReLU)
• Feedforward Neural Networks (FNN)

Intermediate Topics

Deep Learning

• Backpropagation algorithm
• Optimization algorithms (SGD, Adam, RMSprop)
• Initialization techniques (Xavier, He initialization)

Convolutional Neural Networks (CNNs)

• Convolutional layers
• Pooling layers
• CNN architectures (LeNet, AlexNet, VGG, ResNet)

Recurrent Neural Networks (RNNs)

• Long Short-Term Memory (LSTM)
• Gated Recurrent Unit (GRU)
• Bidirectional RNNs

Hyperparameter Tuning

• Grid Search
• Random Search
• Bayesian Optimization

Transfer Learning

• Fine-tuning pre-trained models
• Feature extraction
• Domain adaptation

AutoML

• Automated model selection
• Hyperparameter optimization
• Pipeline optimization

Advanced Topics

Advanced Deep Learning Architectures

• Attention Mechanisms
• Transformer Networks (BERT, GPT)
• Capsule Networks

Reinforcement Learning

• Markov Decision Processes (MDPs)
• Q-Learning
• Policy Gradient Methods (REINFORCE, PPO)

Meta-Learning

• Model-Agnostic Meta-Learning (MAML)
• Learning to Learn
• Few-shot Learning

Explainable AI (XAI)

• Local and global interpretability
• Model-specific and model-agnostic techniques
• Trustworthy AI

Anomaly Detection

• Statistical methods (Z-score, Grubbs' test)
• Machine learning approaches (Isolation Forest, One-Class SVM)
• Deep learning approaches (Variational Autoencoders, GANs)

Graph Neural Networks (GNNs)

• Graph Convolutional Networks (GCNs)
• Graph Attention Networks (GATs)
• Message Passing Networks (MPNs)

Federated Learning

• Decentralized model training
• Privacy-preserving techniques (Differential Privacy)
• Applications in edge computing and IoT

Quantum Machine Learning

• Quantum circuits and gates
• Quantum-inspired algorithms (Quantum Neural Networks, Quantum Support Vector Machines)
• Quantum Annealing

Advanced NLP

• Transformer-based architectures (BERT, GPT, T5)
• Named Entity Recognition (NER)
• Sentiment Analysis
• Language Translation

AI Ethics and Fairness

• Bias and fairness in AI systems
• Ethical considerations in data collection and model deployment
• Responsible AI practices and guidelines