2022 Machine Learning

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Academic unit or major
Undergraduate major in Computer Science
Okazaki Naoaki 
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Tue7-8(W933)  Fri7-8(W933)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

Machine Learning is a "field of study that gives computers the ability to learn without being explicitly programmed" (Arthur Samuel, 1959). With the advances in theories and algorithms, big data, and computation power, machine learning recently showed astonishing progress, applied to various fields other than computer science. This lecture introduces the fundamental concepts and theories of machine learning accompanied by their implementations, which deepens understanding of the theories and applications of machine learning.
* We may prioritize students in the Department of Computer Science, School of Computing, when we need to limit the number of registered students due to the countermeasures for COVID-19.

Student learning outcomes

* Acquire knowledge about the fundamental concepts and theories of machine learning
* Understand the theories and algorithms through implementations
* Learn the basic skill for data processing


regression, classification, neural network, clustering, principal component analysis, regularization, stochastic gradient descent

Competencies that will be developed

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

Class flow

This course consists of lectures explaining the concepts and theories and exercises to confirm and realize them on computers.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction, Essentials of Python programming Introduction to machine learning, Python programming
Class 2 Data Visualization Reading/writing data, line chart, bar chart, plot, heatmap, NumPy, Matplotlib, scikit-learn
Class 3 Linear Regression simple linear regression, least square method, maximum likelihood estimation, multiple linear regression, model selection, regularization, gradient method
Class 4 Linear Binary Classification binary classification, logistic regression, evaluation
Class 5 Linear Multi-class Classification multi-class classification, multi-class logistic regression, softmax function
Class 6 Exercise 1 Exercise for the classes 3-5
Class 7 Neural Networks threshold logic unit, activation function, universal approximation theorem
Class 8 Multilayer Neural Networks (2) computation graph, automatic differentiation, backpropagation, deep neural network, convolutional neural network
Class 9 Support Vector Machine margin, duality, support vectors, kernel functions
Class 10 Exercise 2 Exercise for the classes 7-9
Class 11 Clustering non-hierarchical clustering, K-means, Voronoi diagram, hierarchical clustering, single-linkage clustering, complete-linkage clustering, group averaging method, centroid method, Ward’s method
Class 12 Dimentionality Reduction principal component analysis, singular value decomposition
Class 13 Reignforcement Learning Markov decision process, Bellman equation, value iteration, policy iteration, Q learning
Class 14 Exercise 3 Exercise for the classes 11-13

Out-of-Class Study Time (Preparation and Review)

To enhance the effectiveness of learning, students are encouraged to spend, for each class, approximately 100 minutes for preparing the class in advance and another 100 minutes for reviewing the class afterward (including assignments), using the course material.



Reference books, course materials, etc.

This course uses these web sites:
Machine Learning Notebook: https://chokkan.github.io/mlnote/
Python Quick Reference: https://chokkan.github.io/python/

Assessment criteria and methods

Based on reports (60%) and the final exam (40%).

Related courses

  • CSC.T242 : Probability Theory and Statistics
  • CSC.T272 : Artificial Intelligence
  • CSC.T243 : Procedural Programming Fundamentals
  • CSC.T253 : Advanced Procedural Programming
  • CSC.T352 : Pattern Recognition
  • ART.T458 : Advanced Machine Learning

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

This course expects an understanding of the basic concepts of Linear Algebra, for example, vector, matrix, and eigendecomposition. Because this course uses dynamic web contents for couse materials, it is recommended to have your PC or tablet PC at hand when taking lectures.

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