This course includes introductions to machine learning, its applications for Bioinformatics, and implementations of computational tools for biomedical problems.
本課程包含機器學習技術的介紹及其在生物資訊上的應用, 課程中將帶領學生實作一套解決生醫問題的計算方法.
Molecular biology
The most distinguishing characteristic of living things is their ability to store, utilize, and pass on information. This slide provides a brief introduction or review of the format in which genetic information is maintained and used by living organisms.
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Protein-protein interaction
Introduce the importance of various protein-protein interactions (PPIs) and some fundamental concepts in molecular biology.
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Exercise
Make a classifier for PPI.
Statistics
Statistics is the study of the collection, organization, analysis, interpretation and presentation of data.
There are many similarities between machine learning theory and statistics.
Be familiar with statistics is important in machine learning, and is usually useful in your daily life.
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Exercise
Tests if the selected features are significantly better than other.
Feature selection
Most machine learning algorithms are designed to learn which are the most appropriate features to use for making their decisions. However, in practice, adding irrelevant or distracting features to a dataset often confuses machine learning systems. slide slide
Exercise
Refine your features with feature selection techniques.
RVKDE
This week we will introduce a novel instance-based learning tool, relaxed variable kernel density estimator (RVKDE), based on kernel density estimation techinques.
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Exercise
Regression.
Evaluation
Evaluation is the key to making real progress in machine learning.
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Exercise
Parameter tuning without the web interface.
Feature
Introduce notable issues when extracting features.
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Exercise
Feature encoding.
What is machine learning?
Introduce the basic concepts of machine learning (and finance).
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Exercise
Grab raw data.
Introduction to this course
About this course.
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Exercise
Email TA your name and account name, and join our Facebook club.
See the website of the last year to preview more slides.
歡迎到去年的課程網站預覽更多投影片.
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