Machine Learning Algorithm Design Techniques
( 25 Modules )

Module #1
Introduction to Machine Learning Algorithm Design
Overview of machine learning, importance of algorithm design, and course objectives

Module #2
Mathematical Foundations of Machine Learning
Review of linear algebra, calculus, probability, and optimization techniques

Module #3
Model Selection and Evaluation
Metrics for evaluating machine learning models, overfitting, and underfitting

Module #4
Bias-Variance Tradeoff
Understanding the tradeoff between bias and variance in machine learning models

Module #5
Designing Linear Regression Models
Simple and multiple linear regression, regularization, and feature engineering

Module #6
Designing Logistic Regression Models
Binary and multi-class logistic regression, logistic loss function, and regularization

Module #7
Decision Trees and Random Forests
Decision tree construction, entropy, and information gain, random forests, and ensemble methods

Module #8
Support Vector Machines (SVMs)
Maximum-margin classification, soft margin SVMs, and kernel methods

Module #9
Designing Neural Networks
Introduction to neural networks, perceptron, multilayer perceptron, and backpropagation

Module #10
Convolutional Neural Networks (CNNs)
Architecture, convolutional layers, pooling layers, and applications

Module #11
Recurrent Neural Networks (RNNs)
Architecture, recurrent layers, long short-term memory (LSTM) networks, and applications

Module #12
Unsupervised Learning Techniques
K-means clustering, hierarchical clustering, and dimensionality reduction techniques

Module #13
Dimensionality Reduction Techniques
Principal component analysis (PCA), t-SNE, and autoencoders

Module #14
Anomaly Detection Techniques
One-class SVM, local outlier factor (LOF), and isolation forest

Module #15
Ensemble Methods
Bagging, boosting, and stacking, and ensemble model selection

Module #16
Model Interpretability Techniques
Feature importance, partial dependence plots, and SHAP values

Module #17
Hyperparameter Tuning Techniques
Grid search, random search, and Bayesian optimization

Module #18
Model Selection and Hyperparameter Tuning in Practice
Case studies and best practices for model selection and hyperparameter tuning

Module #19
Handling Imbalanced Datasets
Resampling techniques, cost-sensitive learning, and class weighting

Module #20
Handling Missing Data
Imputation techniques, mean/mode imputation, and multiple imputation

Module #21
Handling High-Dimensional Data
Feature selection techniques, recursive feature elimination, and sparse models

Module #22
Designing for Deployability
Model deployment, model serving, and model monitoring

Module #23
Ethics and Fairness in Machine Learning
Fairness metrics, bias detection, and ethics in machine learning

Module #24
Case Studies in Machine Learning Algorithm Design
Real-world applications of machine learning algorithm design techniques

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning Algorithm Design Techniques career

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