Teaching

Disclaimer

The following resources are a collection of teaching materials used in my classes, which are made available online solely for personal study purpose. They may contain, with or without acknowledgement, copyrighted materials from various sources that represent the intellectual work of other people or organizations. The video materials of some courses are available at YouTube.

In nowadays, high-end CPUs are working at over 100 GFLOPS, several hundred times faster than their ancestors less than two decades ago. The ever increasing demand for computing speed due to the massive amount of data to be processed is driving the evolution of computer architecture towards the era of multi-core and many-core. To fully unleash the power of parallelism, this course is dedicated to the fundamentals of parallel computing and introduces some popular parallel programming schemes, such as the classical MPI and OpenMP for cluster and multi-core computing, the more recent many-core GPU computing with CUDA as well as the Parallel Computing Toolbox in Matlab, one of the most popular scientific computing environments.

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1. Introduction 

2. MPI

3. OpenMP 

Despite of the large volume of data mining papers and tutorials that one can easily collect from the web, it has been surprisingly difficult to find well written ones with a good blend of clarity, technical depth and breadth and enough amount of amusement to make this domain attractive to students with diverse backgrounds. In this course, each module typically starts with an interesting real world example that gives rise to the specific research question of interest. Then, the general idea of how to tackle this problem is presented together with some intuitive and straightforward approaches. Finally, a number of representative algorithms are introduced with concrete examples to show how they function in practice. Theoretical analysis is only adopted as complements to help students better understand the key features of the techniques, instead of making things more complicated than what they should be for most students.

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1. Introduction 

2. Data Warehousing 

3. Data Preprocessing 

5. Classification I 

PPT

PPT

PPT

PPT

PPT

Many people in academia take writing for granted: I am a researcher and so I can write and present my work. However, the flip side is actually true: You need to be effective in writing and presentation in order to progress successfully in your career. Writing is not an instinct but it can be taught and learned. Unfortunately, many non-native English speaking students receive little if any formal training in this aspect. This short course is expected to bring benefits to students in the long term by helping them master various essential skills related to academic writing and presentation, through hands-on practices of the basic rules, underlying principles, common mistakes, useful tips and things that they should always keep an eye on.

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1. Introduction 

2. A Bird's Eye View

3. Professional Writing

4. Literature Review

I have had the privilege of delivering numerous talks on a range of topics spanning data mining, AI, English studies, smart learning, and education. These engagements have drawn diverse audiences, including K-12 students, university faculty, and government officials. It has been incredibly rewarding to interact with individuals from various backgrounds and to share my experiences and insights with them. Witnessing the impact of my talks, where some individuals gain new perspectives and make informed decisions, brings me immense joy.

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1. English Studies 

2. Artificial Intelligence

3. Data Visualization

4. Smart Teaching