Lecture 1. Course Introduction and Practicalities |
The first lecture gives an overview of the course content, structure, and examination. It also give a brief introduction to the programming language Python and related libraries to be used in the course, such as numpy, scipy, matplotlib, scikit-learn, pandas, statsmodels etc. . |
Visit the course room in Canvas, and get familiar with the materials there. Review Lecture 1 slides.
Read the online Python tutorial
https://docs.python.org/3/tutorial/
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Lecture 2. Time-Series Analysis Basics: Part I |
Lecture 2 introduces the basic concepts about time series analysis and data visualization. |
Review Lecture 2 slides.
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Lecture 3. Time-Series Analysis Basics: Part II |
Lecture 3 introduces feature extraction for time-series data including statistical, time-domain, and frequency-domain features etc. |
Review Lecture 3 slides. |
Lab 1. Time Series Visualization and Feature Extraction |
Visualize time series data in various forms and extract features |
Try to complete the lab tasks as much as possible before the lab session.
Use the lab time for Q & A with the lab assistant, and get your lab approved by the lab assistant.
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Seminar 1. Time-Series Data Mining |
Paper presentation and discussion in groups.
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Group work: reading a paper, make slides and orally present the paper. |
Lecture 4. Statistical Time-Series Forecasting: Part I |
Lecture 4 introduces classical statistical time-series models, specifically, the AR, MA, and ARIMA models.
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Read Lecture 4 slides. |
Lecture 5. Statistical Time-Series Forecasting: Part II |
Lecture 5 introduces the Box-Jenkins time-series modeling methodology.
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Read Lecture 5 slides. |
Project work 1 |
Introduce the project work on anomaly detection, and conduct the project work.
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Read the project description and prepare questions, if any. |
Lecture 6. Neural Networks Based Time-Series Forecasting: Part I |
Lecture 6 discusses basic artificial neural networks and their application to time-series prediction.
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Read Lecture 6 slides. |
Lecture 7. Neural Networks Based Time-Series Forecasting: Part II |
Lecture 7 introduces recurrent neural networks and their application to time-series prediction.
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Read Lecture 7 slides. |
Lab 2. ARIMA Model and Prediction |
AR, MA and ARIMA models, and time-series forecasting using the Box-Jenkins methodology.
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Try to complete the lab tasks as much as possible before the lab session.
Use the lab time for Q & A with the lab assistant, and get your lab approved by the lab assistant.
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Seminar 2. Anomaly Detection and AI Challenges in Embedded Systems |
Paper presentation and discussion in groups.
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Group work: reading a paper, make slides and orally present the paper. |
Lecture 8. Statistical Time-Series Clustering |
Lecture 8 introduces un-supervised learning for time-series clustering algorithm, for example, the K-means algorithm.
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Read Lecture 8 slides. |
Lecture 9. Neural Network Based Time-Series Clustering |
Lecture 9 discusses competitive learning based clustering algorithm, in particular, Self Organizing Map.
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Read Lecture 9 slides. |
Lecture 10. Outlook and Course Summary |
Lecture 10 discusses some open challenges such as dependability, sustainability, security in deploying AI in embedded systems from the perspectives of IoT, CPS, and edge-cloud computing paradigm. It then summarizes the course and gives a holistic picture of the course content.
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Read Lecture 10 slides. |
Lab 3. Time-Series Clustering |
Implement, evaluate, and application of the clustering algorithms such as K-means and SOM etc. for time-series clustering.
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Try to complete the lab tasks as much as possible before the lab session.
Use the lab time for Q & A with the lab assistant, and get your lab approved by the lab assistant.
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Project work 2 |
Conduct project work, and have Q & A with lab assistants.
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Try to complete the project tasks as much as possible before the project work session.
Use the project work time for Q & A with the lab assistant, and get your project results validated by the lab assistant.
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Workshop |
Present project work per group in the workshop.
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Group work: Make slides and present your project.
Each group member shall contribute to and present part of the presentation slides.
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