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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

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15. Project presentations & Exam

15. Project presentations & Exam#

Information will be communicated by email in due time.

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1. Linear Regression in Python

By Claire David
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