Syllabus

Introduction to Data Science and ML
• What is Data Science? Machine Learning overview
• Use cases in Engineering: predictive maintenance, fault detection, optimization

Data Preprocessing and Exploration
• Handling time-series and sensor data
• Missing data, normalization, noise filtering
• Feature engineering and correlation analysis
• Visualization: trends, anomalies, and decision regions

Unsupervised Learning
• Clustering: K-means, DBSCAN for sensor fusion and fault grouping
• Dimensionality Reduction: PCA for signal compression
• Model selection, cross-validation, and hyperparameter tuning

Supervised Learning (Regression and Classification)
• Linear Regression, Polynomial Regression, Suport Vector Regression, Ridge, Lasso
• Neural Network (NN), Recurrent Neural Network (RNN), LSTM
• Logistic Regression, k-NN, Decision Trees, Random Forest
• ML for system identification and fault classification
• Model evaluation: R², MSE, confusion matrix, ROC

Time-Series Modeling and Predictive Analytics
• Time-series forecasting (ARIMA, Exponential Smoothing)
• Introduction to state-space modeling using ML
• Data-driven predictive control concepts

Reinforcement Learning
• Basics of RL: agent, environment, rewards
• Q-learning and policy learning
• Applications in adaptive control and optimization
• Comparison with traditional feedback control (PID)

Understand the foundational concepts of data science and machine learning in engineering systems.

Analyze and process time-series data and system signals from engineering applications.

Apply supervised and unsupervised learning to real-world engineering problems.

Integrate machine learning techniques into system identification, diagnostics, fault detection, and predictive control.

Gain hands-on experience using Python, Scikit-learn, and essential data science libraries/MATLAB.

Apply basic principles of data preprocessing, feature selection, and visualization in engineering datasets.

Formulate and solve classification and regression problems relevant to engineering systems using ML algorithms.

Develop unsupervised learning models to cluster sensor data and detect anomalies.

Implement model-based and data-driven predictive algorithms for engineering applications.

Design simple decision-making systems using reinforcement learning algorithm.

Use Python libraries (NumPy, pandas, matplotlib, scikit-learn)/MATLAB to solve engineering ML problems.

Andreas C. Müller, Sarah Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists, O'Reilly Media, Inc., 2016

Richard S. Sutton, Andrew G. Barto , Reinforcement Learning: An introduction. , MIT press, 1998.

Lennart Ljung, System Identification: Theory for the User, Pearson Education, 1998

Andrew Ng, Machine Learning Yearning: Technical Strategy for AI Engineers in the era of Deep Learning, https://www. mlyearning. org (2019).

Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, John Wiley & Sons, 2012