ACIT4530 Data Mining at Scale: Algorithms and Systems Emneplan

We are witnessing the era of Big Data where data is generated, collected, and processed at an unprecedented scale and data-driven decisions influence many aspects of modern life.

Data mining is the process of discovering patterns in large data sets involving methods in statistics and database systems. A large number of applications such IoT sensors generate large amounts of data streams. The necessity of data stream mining and learning from the data is increasingly becoming more prevalent and urgent.

Extracting knowledge from data sets requires not only computational power but also programming abstractions as well as analytical skills. In this course, the students will be exposed to the different approaches for data mining and stream processing such as association rules learning, anomaly detection, data clustering, visualizations, and extracting statistical features on the fly from large data streams. The students will be exposed to concrete data mining and neural network architectures including deep learning models for handling large data streams such as convolutional neural networks, recurrent neural networks, autoencoders, transformers and attentions. In this course, the student will also be exposed to different data mining systems, working end-to-end pipelines including performance evaluation, detecting overfitting, underfitting, and data defects. With a focus on data mining applications, we will study some powerful numerical linear algebra methods.

It is an advantage to have some experience with the following subjects:

• Mathematical Analysis
• Basic programming, such as scripting
• Statistics, specifically probability theory

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The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

• has a deep understanding of how data mining can be used to extract knowledge from data sets.
• has advanced knowledge of the different data mining algorithms

Skills

Upon successful completion of the course, the student:

• can design and implement data mining algorithms
• can deploy different data mining systems and configure them
• can utilize a specialized library for data mining

General competence

Upon successful completion of the course, the student:

• can use data mining systems to mine data
• can analyse data mining solutions with regard to robustness and in relation to his/her intended tasks
• can explain how data mining can be used in different applications areas such as business analytics

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Two internal examiners. External examiner is used periodically.

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