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 organization.


Advanced Computing Course

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.

Title

Description

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1. Introduction 

Fundamental concepts and supporting techniques of parallel computing PPT

2. MPI

Parallel Programming with Distributed Memory PPT

3. OpenMP 

Parallel Programming with Shared Memory PPT
4. GPU Computing Fundamentals of CUDA programming and advanced techniques for performance optimization PPT

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Data Mining Course

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.

Title

Description

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1. Introduction 

Essential concepts, techniques and applications of Data Mining PPT

2. Data Warehousing 

Data Warehouse, Data Mart, ETL, Metadata, Data Cube, Star/Snowflake/Galaxy Schema, OLAP PPT

3. Data Preprocessing 

Missing Value, Duplicate Data, Transformation, Visualization, PCA, LDA PPT
4. Regression Analysis Simple Linear Regression, Polynomial Regression, R2 PPT

5. Classification I 

K-Nearest Neighbor, Naive Bayes Classifier  PPT 
6. Classification II  Hidden Markov Model, Decision Tree Model PPT
7. Classification III Artificial Neural Networks

PPT

8. Classification IV Support Vector Machines

PPT

9. Clustering K-Means, K-Medoids, Leader, Hierarchical Clustering, Density Methods, EM Algorithm

PPT

10. Association Rule Frequent Itemsets, Association Rules, Apriori Algorithm, Sequential Pattern Mining

PPT

11. Recommendation Algorithms TF-IDF, Latent Semantic Analysis, PageRank, Collaborative Filtering PPT
12. Ensemble Learning Bagging, Stacking, Boosting, Random Forest, AdaBoost, RegionBoost

PPT

13. Evolutionary Algorithms Optimization, Genetic Algorithms, Genetic Programming, Evolutionary Arts, Demo PPT
14. Active & Reinforcement Learning Uncertainty Sampling, Query-By-Committee, Co-Testing, Q-Learning, Temporal Difference Learning PPT
15. Beautiful Data Real-World Cases of Data Analysis PPT

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Academic Writing Course

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.

Title

Description

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1. Introduction 

Practical skills for effective English language learning PPT

2. A Bird's Eye View

Everything you need to know for writing and presenting your research work PPT

3. Professional Writing

Language, Sentence and Paragraph PPT

4. Literature Review

Annotated Bibliography, Literature Review and EndNote  PPT 
5. Job Application Letters Curriculum Viate, Cover Letter, Selection Criteria PPT
6. Public Speaking & Communication Speech, Interpersonal and Interview Skills PPT