2022 From Data Analytics to Machine Learning

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Academic unit or major
Graduate major in Human Centered Science and Biomedical Engineering
Slavakis Konstantinos 
Class Format
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Course description and aims

This class touches upon the basics of machine learning (ML). Hands-on experience with a minimal exposition to the underlying mathematics. Rather than detailing the math, the focus is placed on the motivation and goals behind ML algorithms. Students will learn to use ML algorithmic tools via numerous examples of Python (scikit-learn) code and life-sciences data. This class serves as a stepping stone between Introduction to Data Science (HCB course, 1Q) and more advanced and mathematically oriented ML, data-science and AI classes.

Student learning outcomes

Learn the basic concepts of machine learning. Learn to use Python to run basic machine-learning algorithms.


Machine learning, data analytics, Python

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills
(1) Learn how to run basic ML algorithms via Python (scikit-learn) code and life-sciences data. (2) Build intuition to identify the appropriate ML algorithm for the data-science problem at hand.

Class flow

Basic concepts will be explained in class. Hands-on approach: Run basic machine-learning algorithms in real time during classes. Homeworks will be projects based on the Python code explained in class. All slides, Python code and data will be provided by the instructor.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Course introduction Course logistics. What is machine learning? What are its goals? Installing Python (offline/online) and using scikit-learn.
Class 2 Introduction to regression Linear (least-squares), regularized, and polynomial regression
Class 3 Introduction to dimensionality reduction Principal component analysis (PCA) and variants
Class 4 Introduction to clustering Kmeans and spectral clustering
Class 5 Introduction to classification (I) Naive Bayes and support vector machines (SVMs
Class 6 Introduction to classification (II) Random forests and the perceptron (intro to neural networks)
Class 7 Introduction to deep neural networks Convolutional neural networks, recurrent neural networks, etc

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

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.



Reference books, course materials, etc.

All slides, Python code and data will be provided by the instructor.

(1) A. Geron, “Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow,” O’Reilly, 2nd ed., 2019.
(2) R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” John Wiley & Sons, 2nd ed., 2001.
(3) S. Theodoridis, “Machine Learning: A Bayesian and Optimization Perspective,” Academic Press, 2nd ed., 2020.

Assessment criteria and methods

Homeworks/assignments (100%)

Related courses

  • Introduction to Data Science(HCB)

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

Very basics of calculus. No knowledge of Python is required.

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

slavakis.k.aa[at]m.titech.ac.jp / 045-924-5410

Office hours

Every Tuesday (16:00-17:00) via Zoom

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