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.


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, Tansformation, 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 Artifical Neural Networks

PPT

8. Classification IV Support Vector Machines

PPT

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

PPT

10. Association Rule Frequent Itemsets, Association Rules, Apriori Algorithm

PPT

11. Ensemble Learning Bagging, Stacking, Boosting, Random Forest, AdaBoost, RegionBoost

PPT

12. Evolutionary Algorithms Optimization, Genetic Algorithms, Genetic Programming, Evolutionary Arts PPT
13. Active & Reinforcement Learning Uncertainy Sampling, Query-By-Committee, Co-Testing, Q-Learning, Temporal Difference Learning 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 

Course Profile PPT

2. A Bird's Eye

Everything you need to know for writing and presenting your research work (3 hours) PPT

3. Professional Writing

Language, Sentence and Paragraph (2 hours) PPT

4. Literature Review

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