2016 Machine Learning

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Academic unit or major
Graduate major in Artificial Intelligence
Murata Tsuyoshi  Terano Takao 
Course component(s)
Day/Period(Room No.)
Tue3-4(W631,J232)  Fri3-4(W631,J232)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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Course description and aims

This course introduces basic knowledge of machine learning and data mining.

Student learning outcomes

[Goal] The goal of this course is to learn basic concepts and methods (such as classification, association, clustering and numeric prediction) for mining huge data observed in real world. Basic concepts and methods in machine learning and data mining are explained, and tools for promoting understanding are also introduced in this course.
[Theme] The following themes are mainly explained: input and output data formats for machine learning, machine learning algorithms, evaluation methods for learning algorithms, and methods for handling missing / noisy data in real world.


attribute, instance, bias, overfitting, missing value, exception, supervised learning, unsupervised learning, decision tree, information gain, pruning, classification, naïve bayes, association rule, apriori algorithm, numeric prediction, regression, instance-based learning, clustering, k-means algorithm, hierarchical clustering, support, confidence, cross-validation, bootstrap, significance test, confusion matrix, ROC curve, MDL principle, support vector machine, EM algorithm

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills
- - - -

Class flow

Tools for machine learning are explained in the lectures to promote understanding.

Course schedule/Required learning

  Course schedule Required learning
Class 1 introduction machine learning and data mining, and a tool (Weka)
Class 2 concept description, applications of machine learning Representations and applications of machine learning
Class 3 concept space, biases biases of each representation
Class 4 input data format, classification, association input format of classification and association
Class 5 clustering, numeric prediction input format of clustering and numeric prediction
Class 6 attribute types and their transformations characteristics of attributes for representation
Class 7 knowledge representation, decision tree, classification rule representation of decision tree and classification rule
Class 8 association rule, instance-based representation representation of association rule and instance-based learning
Class 9 basic learning algorithm, Naive bayes Naive bayes learning
Class 10 decision tree, information gain, gain ratio decision tree learning
Class 11 covering algorithm, rule and decision tree decision rule learning and comparison with decision tree
Class 12 evaluation of learning methods, cross validation evaluation of learning and usage of data
Class 13 tistatistic, minimum description length comparison of machine learning methods
Class 14 ROC curve, recall, precision evaluation metrics for learning methods
Class 15 support vector machine, EM algorithm SVM and EM algorithm


Data Mining: Practical Machine Learning Tools and Techniques (Third Edition)
I. H. Witten, E. Frank, Morgan Kaufmann, 2011.

Reference books, course materials, etc.

Specified in the class.

Assessment criteria and methods

Course scores are based on assignments(70%) and quizzes(30%).

Related courses

  • MCS.T507 : Theory of Statistical Mathematics
  • MCS.T403 : Statistical Learning Theory

Prerequisites (i.e., required knowledge, skills, courses, etc.)




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