Lecture 1. Course Introduction and Practicalities |
The first lecture gives an overview of the course content, structure, and examination. It also introduces the programming language Python and related libraries to be used in the course, such as numpy, scipy, matplotlib, scikit-learn, pandas, statsmodels, keras, 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 of time series analysis and data visualization. |
Review Lecture 2 slides.
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Lecture 3. Time-Series Analysis Basics: Part II |
Lecture 3 discusses feature extraction for time-series data including statistical, time-domain, and frequency-domain features, etc. |
Review Lecture 3 slides. |
Lab 0. Tool installation and environment setup |
This is a preparation lab for those students, who have issues or need help with experiment setup. |
Download, install Anaconda https://www.anaconda.com/ , and all necessary libraries on your own laptop. Test if the installations are successful. |
Lab 1. Time Series Visualization and Feature Extraction |
Visualize time series data in various forms and extract relevant 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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Lecture 4. Statistical Time-Series Modeling and 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 Modeling and Forecasting: Part II |
Lecture 5 introduces the Box-Jenkins time-series modeling methodology and forecasting.
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Read Lecture 5 slides. |
Lecture 6. Neural Networks Based Prediction: 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 Prediction: Part II |
Lecture 7 introduces recurrent neural networks and their application to time-series prediction.
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Read Lecture 7 slides. |
Seminar 1. Time-Series Data Mining and Anomaly Detection |
Paper presentations in groups and discussion.
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Group work: reading a paper, making slides, and orally presenting the paper. Also acting as opponent to another paper presentation in Q & A. |
Project Introduction |
Introduce the project work, and conduct the project work.
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Read the project description and prepare questions for discussions, if any. |
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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Lecture 8. Statistical Clustering |
Lecture 8 introduces un-supervised statistical learning for clustering, specifically, the K-means algorithm and hierarchical clustering. In addition, we introduce dynamic time warping (DTW).
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Read Lecture 8 slides. |
Lecture 9. Neural Network Based Clustering |
Lecture 9 discusses the competitive learning based clustering algorithm, in particular, Self Organizing Map (SOM).
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Read Lecture 9 slides. |
Lecture 10. Outlook, AI Dependability and Course Summary |
In Lecture, 10 we discuss some issues of deep learning in pattern recognition, AI dependability and explainability. We then review the key learning points (KLPs) of the course and give a holistic picture of the entire course.
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Read Lecture 10 slides. |
Lab 3. Clustering with K-means and SOM, Similarity with DTW |
Implement, evaluate, and application of the clustering algorithms such as K-means and SOM, etc.
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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 presentations in groups and discussion.
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Group work: reading a paper, making slides, and orally presenting the paper. Acting as opponent to another paper presentation in Q & A. |
Project work 1 |
Conduct project work, and have Q & A with lab assistants.
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Use the project work time for Q & A with the lab assistant, and get your project results validated by the lab assistant. |
Project work 2 |
Conduct project work, and have Q & A with lab assistants.
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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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Project Workshop |
Present your project work in the workshop.
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Individual work as part of examination: Make slides and present your project. Be prepared to answer questions related to the project.
(Two sessions, sign-up and attend one of the two sessions)
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