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fiscal 2020 : Data Mining Theory

Syllabus: [ Japanese | English ]
Contact: see also syllabus.
References
Lecture documents
- #1 : Guidance
- Case 1: Introduction to data mining
  - #2 : What is Data Mining? Some definitions, simple examples and applications.
  - #3 : Machine Learning and Statistics, aspects of personal information.
  - #4 : Instances and Attributes as Input, and Knowledge Representations as Output
  - #5 : The basic methods and algorithms 1
  - #6 : The basic methods and algorithms 2
  - #7 : Credibile experiment designs and evaluation ways
- Case 2: Introduction to machine learning
  - #2 : What is Machine Learning?
  - #3 : Classification by perceptron learning
  - #4 : Classification models in Scikit-learn
  - #5 : Building good training sets and pre-processing
  - #6 : Compressing data via dimensionality reduction
  - #7 : Model evaluation and Hyperparameter tuning
- #8 : reserve day (e.g., adjustments for Part 2)
- Part 2: Discussion about applications
  - #9 : readings 1
  - #10 : readings 2
  - #11 : readings 3
  - #12 : readings 4
  - #13 : readings 5
  - #14 : readings 6
  - #15 : readings 7

#1 : Guidance

Google drive (required @ie.u-ryukyu.ac.jp or authorized google account)
[Lecture documents]
- ~~Guidance: Data Mining Theory: Guidance~~
- Guidance: Data Mining Theory: Guidance
~~grouping~~
- 2 groups (presentations) for each week.
  max 45 minutes for one group. Each group has 30-35 minutes for presentation, 10 minutes for discussion.
assignments: see assignment page on the google drive also.
- choose book1 or book2 by voting (majority).
  
  book1: ref1-DM-TOC.pdf (Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition)
  book2: ref2-ML-TOC.pdf (Python Machine Learning, Second Edition)
- ~~make an assignment by randamly.~~
Homework
- read and ready your assigned references to explain/introduce. You must make any presentation documents and uploat them to google documents until the previous day.
- e.g., PowerPoint (slides), commentary documents, demos, etc.

References

Books
- Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition
Frameworks or Tools to use Machine Learning
- Weka 3: Data Mining Software in Java
- scikit-learn: Machine Learning in Python
- The R Project for Statistical Computing
- Apache Spark MLlib
- Caffe
- Chainer
- Google TensorFlow
- SkFlow for Tensorflow
- CNTK: Computational Network Toolkit by Microsoft
- DSSTNE: Deep Scalable Sparse Tensor Network Engine by Amazon
Others
- The 38 best tools for data visualization (Chart.js, Tableau,,,)