Advanced Machine Learning Techniques

About Course

This course moves beyond foundational algorithms to
delve into sophisticated machine learning methods
crucial for complex real-world data challenges. Students
will develop expertise in cutting-edge techniques to
significantly enhance model performance, address
intricate data characteristics such as imbalance or high
dimensionality, and build robust solutions for
specialized prediction tasks. The curriculum provides a
deep theoretical understanding paired with practical,
hands-on experience in optimizing and deploying these
advanced approaches across diverse industry contexts.

Master Ensemble Methods: Design and implement powerful ensemble models like XGBoost and Random Forests to achieve superior prediction accuracy and stability across various datasets.
Optimize Feature Engineering: Apply advanced feature selection (e.g., Boruta, Recursive Feature Elimination) and creation techniques (e.g., polynomial features, interaction terms) to maximize predictive power from raw data.
Handle Complex Datasets: Develop robust strategies for learning from imbalanced classes (e.g., SMOTE, ADASYN), sparse data, and high-dimensional
features commonly found in genomics or natural language processing.
Advanced Optimization Strategies: Utilize state-of-the-art optimization algorithms (e.g., AdamW, L-BFGS) and hyperparameter tuning techniques (e.g.,
Bayesian Optimization with Optuna) to efficiently train and fine-tune complex deep learning and traditional ML models.
Solve Structured Prediction Problems: Implement specialized algorithms such as Conditional Random Fields (CRFs) for tasks like sequence labeling (e.g.,
Named Entity Recognition) and multi-output regression.
Evaluate Model Trade-offs: Critically assess the balance between model complexity, predictive performance, computational cost, and interpretability using metrics like SHAP values and LIME, informing real-world deployment decisions.

Course Content

Ensemble Learning Architectures
Dive deep into advanced ensemble methods. We will implement and compare Bagging (e.g., Random Forests, Extra Trees for improved stability) and Boosting algorithms (e.g., AdaBoost, Gradient Boosting, XGBoost, LightGBM, and CatBoost for cutting-edge performance). Learn to combine diverse models through Stacking and explore Voting classifiers, understanding how to maximize diversity and minimize bias for robust predictions.

Intelligent Feature Engineering
Go beyond basic transformations to master automated feature extraction using techniques like Deep Feature Synthesis. Explore advanced feature selection methods, including filter-based (e.g., mutual information), wrapper-based (e.g., Recursive Feature Elimination), and embedded methods (e.g., Lasso regularization). Learn robust feature construction, advanced dimensionality reduction (e.g., t-SNE, UMAP), and practical strategies for feature importance analysis and interpretation in domain-specific applications like financial forecasting or medical diagnostics.

Mastering Imbalanced Data
Confront the pervasive challenge of imbalanced datasets (e.g., fraud detection, rare disease diagnosis) using advanced resampling techniques like SMOTE-NC for categorical features and ADASYN. Implement cost-sensitive learning algorithms, explore robust anomaly detection approaches, and master one-class classification for outlier identification. Critically evaluate performance using appropriate metrics beyond accuracy, such as F1- score, Precision-Recall curves, and Matthews Correlation Coefficient.

Advanced Model Optimization & Tuning
Examine sophisticated gradient descent variants (e.g., AdamW, Ranger) and delve into second-order optimization methods (e.g., L-BFGS, conjugate gradient). Master cutting-edge hyperparameter optimization techniques including Grid Search, Randomized Search, and advanced Bayesian Optimization frameworks (e.g., Optuna, Hyperopt). Implement multi-objective optimization for trade- offs and dynamic learning rate scheduling strategies like cyclical learning rates and cosine annealing.

Solving Structured Prediction Problems
Learn to model and predict complex, interdependent outputs. This module covers sequence labeling with Conditional Random Fields (CRFs) for tasks like Named Entity Recognition, Structured Support Vector Machines (Structured SVMs) for image segmentation, and energy-based models. Explore graphical models for structured data, multi-label and multi-output learning, and advanced ranking/preference learning algorithms for recommendation systems.

Bayesian & Probabilistic Models
Gain expertise in Bayesian machine learning for robust uncertainty quantification, Gaussian processes for regression with confidence intervals, and variational inference for intractable posteriors. Cover Hidden Markov Models (HMMs) for time-series analysis (e.g., speech recognition), Topic Modeling with Latent Dirichlet Allocation (LDA) for document analysis, and advanced probabilistic graphical models (e.g., Factor Graphs), including practical applications in Bayesian optimization and A/B testing.

Capstone Project
Students will tackle a real-world, highly challenging machine learning problem—for example, predicting rare disease outbreaks from heterogeneous medical records or detecting subtle financial fraud patterns in imbalanced transaction data. You will design and execute rigorous experiments, comparing and combining multiple advanced techniques such as custom ensemble architectures, iterative feature engineering pipelines, and specialized optimization strategies. The goal is to identify optimal solutions that demonstrate superior performance on complex datasets, culminating in a comprehensive technical report detailing your methodology, experimental design, results, and critical evaluation of model trade-offs and generalizability.

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Access and gain proficiency with advanced machine learning libraries such as Scikit-learn (ensembles, feature selection), XGBoost, LightGBM, CatBoost, TensorFlow Probability, PyTorch Geometric, and specialized frameworks for
hyperparameter optimization (Optuna, Hyperopt).
Utilize benchmark datasets from Kaggle competitions and academic sources (e.g., UCI Machine Learning Repository) that exemplify complex data challenges. Engage with seminal research papers from top-tier conferences (NeurIPS, ICML, KDD) on state-of-the-art methods, and leverage advanced visualization libraries (e.g., Yellowbrick, SHAP, LIME) for in-depth model inspection and interpretation.

Advanced Machine Learning Techniques

Advanced Machine Learning Techniques

About Course

What Will You Learn?

Course Content

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Advanced Machine Learning Techniques

Advanced Machine Learning Techniques

About Course

What Will You Learn?

Course Content

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