Machine Learning

Jacky Baltes

National Taiwan Normal University (NTNU)

Taipei, Taiwan

jacky.baltes@ntnu.edu.tw

Important Updates

• Sat. 20th Feb. 2021 20:12:54 CST. Page opened.

• Class Time: To be determined

• Join the http://www.discord.com server NTNU https://discord.gg/6MyURYyu. Send a message !register <student_id>machine-learning to the @NTNU Online Lecturing Support bot

Content

This course covers a variety of topics in machine learning. This includes both supervised and unsupervised approaches. The following algorithms will be covered:

• Regression

• Bayesian Learning

• EM algorithm

• Support Vector Machines

• Neural Networks

• Clustering

• Ensemble Techniques

There is a large practical component to the course. You will implement various machine learning algorithms using python and its associated machine learning libraries (matplotlib, scikit, panda, tensorflow, pytorch, …).

Assessment

Your grade in the course depends on the following assessments:

1. Assignment 1: 10%

2. Midterm Test: 20%

3. Project: 30%

4. Final Exam: 40%

Lecture Slides

The course uses a combination of traditional lecture slides and interactive jupyter notebooks. Some of the notebooks are hosted on the Google Colaboratory and Microsoft Azure Notebooks servers.

The following lecture slides are available:

• Introduction to Python Programming

• Introduction to Machine Learning

• Decision Trees

• Linear Regression

• Logistic Regression

• Validation

• Support Vector Machines

• Bayesian Learning

• Ensemble Techniques and Boosting

Assignments and Projects

Assignment and projects must be submitted via the @NTNU Lecturing Bot.

You must submit an honesty declaration for each assignment or project. You must cite all external sources that you used during the preparation of your assignment or project in the honesty declaration.

Sample Code

Link to Jupyter Notebooks