Course Logistics

1. The course will take place on Monday and Wednesday afternoons beginning on 19/09/2022 as follows (Please check the synapses platform to see which group (Gr) you were assigned to, to determine your lab time and location):

• Monday (14:00 - 16:00) magistral teaching in: Amphi Cauchy.
• Monday (16:15 - 18:15) lab sessions in the small classes: Amphi Painlevé (Gr1), Amphi Poisson (Gr2), Amphi Sauvy (Gr3).
• Wednesday (14:00 - 16:00)  lab sessions in the small classes: PC11 (Gr4), PC22 (Gr5), PC12 (Gr6).

2. Interaction/Q&As: 

We have set up a dedicated slack workspace for this course, which we will use to communicate with you. Please register to the following workspace with your full name and use it as much as possible.

https://join.slack.com/t/slack-ymb9142/shared_invite/zt-1fi2zfe6r-RmTe4OVpq6xJKSpcn9DNJg

3. The course will be assessed via:

• (20% of the course mark) an individual take-home assessment handed out on Monday 3rd October with deadline on Monday 17th October 2pm.
• (80% of the course mark) a group data challenge handed out on Monday 7th November with a written report deadline on Wednesday 7th December 5pm and oral assessments in the week of the 12th December. We will communicate further details about the formation of groups for the challenge at the start of November on the course slack channel.

4. You will have to complete the course work on your own laptops (preferably with a Unix environment like Linux or Mac OS X for compatibility reasons). As for software, we will be using  Python among others (to be installed locally on the laptops). Students are invited to install the current Anaconda distribution BEFORE the 1st lab session (https://www.anaconda.com/products/individual). Please also make sure that you have installed the PyTorch, numpy and scikit-learn python packages. 

Detailed syllabus of the course

(minor changes may apply during the course progression.)

General Introduction to Machine Learning

- Machine Learning paradigms
- The Machine Learning Pipeline

Supervised Learning

- Generative and non generative methods
- Naive Bayes, KNN and regressions
- Tree based methods

Unsupervised Learning

- Dimensionality reduction
- Clustering 

Advanced Machine Learning Concepts

- Regularization
- Model selection
- Feature selection
- Ensemble Methods

Kernels

- Introduction to kernels
- Support Vector Machines

Neural Networks

- Introduction to Neural Networks
- Perceptrons and back-propagation

Deep Learning I

- Convolutional Neural Networks
- Recurrent Neural Networks
- Applications

Deep Learning II

- Modern Natural Language Processing
- Unsupervised Deep Learning
- Embeddings, Auto-Encoders, Generative Adversarial Networks

Machine & Deep Learning for Graphs

- Graph Similarity 
- Graph Kernels 
- Node Embeddings