Machine Learning

This repository contains introductory lectures on Machine Learning.

Lectures

Basic concepts: This lecture gives an overview of basic concepts in machine learning (learning paradigms, training notions, illustration using the least-square problem)
No deep Learning: This lecture contains examples of various non-deep learning models such as logistic regression, support vector machines, random forest, etc. The basic mathematical formulations and illustrative examples are given for each models.
Intro to Deep Learning: This lecture gives an introduction to neural networks (the perceptron, the multi-layer perceptron, backpropagation, convolutional models, etc.). Once again, the basic mathematical formulations and illustrative examples are given for each concept.
Intro to UNet/ResNet/DenseNet
Advanced Deep Learning Concepts

Libraries:

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
images		images
.gitignore		.gitignore
Lect_01.ipynb		Lect_01.ipynb
Lect_02.ipynb		Lect_02.ipynb
Lect_03.ipynb		Lect_03.ipynb
Lect_04-tf.ipynb		Lect_04-tf.ipynb
Lect_04.ipynb		Lect_04.ipynb
Lect_05.ipynb		Lect_05.ipynb
README.md		README.md
_config.yml		_config.yml
lecture_1_bis.ipynb		lecture_1_bis.ipynb
lecture_1_bis_2.ipynb		lecture_1_bis_2.ipynb
lecture_custom_loss.ipynb		lecture_custom_loss.ipynb
lecture_model_combining.ipynb		lecture_model_combining.ipynb
lecture_model_combining_v2.ipynb		lecture_model_combining_v2.ipynb
lecture_unet.ipynb		lecture_unet.ipynb
retina_prep.ipynb		retina_prep.ipynb

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