Toggle navigation sidebar

Toggle in-page Table of Contents

Introduction
to Machine Learning

Introduction to Machine Learning

About this course

Learning outcomes
Instructor & Schedule
JupyterHub for class
Evaluation

Week 1

1. Course Trailer
2. Warm up: Linear Regression
3. Classification algorithms
4. Decision Trees and Boosting
5. Review Week 1

Week 2

6. Neural Networks Part I
7. Neural Networks Part II
8. Towards Deep Learning Models
9. Convolutional Neural Networks
- Learn CNNs
- Guest Lecture
10. Review Week 2

Week 3

11. Unsupervised Learning Part I
12. Unsupervised Learning Part II
- Autoencoders
- Variational Autoencoder for Anomaly Detection
13. Big Data & ML Strategies
- Big Data
- Strategies in Machine Learning
14. Ethics in ML & Outlook
- Ethics in ML
- Outlook
15. Project presentations & Exam

Tutorials

1. Linear Regression in Python
2. ‘Forestree’ with LHC collisions
3. Classification Contest
4. Anomaly Detection

Powered by Jupyter Book

.md

15. Project presentations & Exam

15. Project presentations & Exam#

Information will be communicated by email in due time.

previous

Outlook

next

1. Linear Regression in Python

By Claire David
© Copyright 2022.

All content on this site (unless otherwise specified) is licensed under the CC BY-NC-SA 4.0 license