Unit 898: Comprehensive Introduction to Machine Learning

1. Introduction to Machine Learning

• Definition and overview of machine learning
• Applications across industries (e.g., healthcare, finance, autonomous systems)
• Types of machine learning
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
• Differences between traditional programming and machine learning
  • Rule-based vs data-driven approaches

2. Supervised Learning Algorithms

• Concept of labeled data and target variables
• Overview of common supervised algorithms:
  • Linear Regression
    • Model assumptions
    • Use cases and limitations
  • Logistic Regression
    • Binary classification
    • Interpretation of coefficients
  • Decision Trees
    • Tree structure and splitting criteria
    • Overfitting and pruning
  • Support Vector Machines (SVM)
    • Margin maximization
    • Kernel trick
  • Neural Networks
    • Basic architecture (neurons, layers)
    • Activation functions
    • Training with backpropagation

3. Unsupervised Learning Algorithms

• Understanding unlabeled data
• Clustering Algorithms
  • k-Means Clustering
    • Algorithm steps
    • Choosing number of clusters
  • Hierarchical Clustering
    • Agglomerative and divisive approaches
    • Dendrogram interpretation
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
    • Variance explained
    • Data visualization benefits
• Association Rule Learning
  • Apriori algorithm basics
  • Market basket analysis examples

4. Reinforcement Learning

• Introduction to reinforcement learning concepts
• Markov Decision Processes (MDP)
  • States, actions, rewards, policies
• Q-Learning
  • Value iteration
  • Exploration vs exploitation
• Policy Gradients
  • Gradient ascent on expected reward
• Deep Reinforcement Learning
  • Combining neural networks with RL
  • Examples (e.g., Deep Q-Networks)

5. Model Evaluation and Selection

• Evaluation metrics
  • Accuracy
  • Precision, Recall, F1 Score
  • ROC Curve and AUC
• Cross-validation techniques
  • k-Fold cross-validation
  • Stratified sampling
• Hyperparameter tuning
  • Grid search
  • Random search
  • Bayesian optimization

6. Feature Engineering

• Importance of feature engineering in ML
• Feature selection techniques
  • Filter, wrapper, embedded methods
• Feature transformation
  • Normalization and standardization
  • Log transformations
• Feature creation
  • Polynomial features
  • Interaction terms
• Feature scaling
  • Min-max scaling
  • Robust scaling

7. Model Deployment and Scaling

• Deployment best practices
  • Model serialization (e.g., pickle, ONNX)
  • API development for model serving
• Scalability considerations
  • Batch vs real-time inference
  • Cloud services and containerization
• Monitoring and maintenance
  • Performance monitoring
  • Model drift detection

8. Ethical Considerations in Machine Learning

• Bias and fairness
  • Sources of bias
  • Techniques to mitigate bias
• Transparency and explainability
  • Interpretable models
  • Explainable AI (XAI) methods
• Privacy concerns
  • Data anonymization
  • Differential privacy
• Responsible AI practices
  • Ethical guidelines
  • Regulatory compliance