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AI Workshops

AI workshops to introduce the student to AI with exercises in notebooks.

You will need to install Jupyter Notebook. We recommend you also use Anaconda to avoid any dependencies issue.

In each directory, you will find the exercises, a README explaining the purpose of the workshop and the slides used during the workshop.

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0 - Python3 & Jupyter Notebook 🐍

Basics of python and usage of Jupyter Notebook. You will:

  • Learn to use iPython notebooks
  • Learn Python Syntax
  • Solve complex algorithm exercises
  • Master the different programming paradigms of Python

1 - Data Science & Pandas basics 👨‍🔬

Basics of Data Science and introduce Pandas. You will:

  • learn to use Pandas
  • analyse huge datasets
  • clean datasets
  • visualize data analyse

2 - Logistic Regression 💻

Creation of your first classification method. You will:

  • initialize hyperparameters
  • calculate of the loss function and usage of gradient
  • usage of optimizing algorithm

3 - Hidden Layer 📈

Discover the effect of a hidden layer. You will:

  • create a neural network with a hidden layer
  • use neurons with a nonlinear activation function (tanh)
  • calculate the loss cross-entropy
  • implement forward and backward propagations

4 - Tensorflow 📙

Usage of Tensorflow. You will:

  • create a Sequential model
  • use Dense layers
  • predict house pricing
  • classify fashion_mnist
  • classify mnist

5 - Convolution 🔲

Discover the effect and usage of convolution. You will:

  • understand the point to use convolutional neural network
  • use Conv2D
  • use Maxpooling2D

6 - Value function 🏡

Basics of Reinforcement Learning. You will:

  • understand the Markov Decision Process
  • use the value function
  • solve the Antic maze

7 - Q-Learning 🚙

Continuation of Reinforcement Learning. You will:

  • use Gym environment
  • use Q learning
  • solve Mountain-Car-V0

8 - Decision Tree 🌲

Discover one of the most popular supervised learning methods for classification problems. You will:

  • Use Scikit-learn
  • Create a Decision Tree
  • Create a Decision Tree Forest

9 - Predict house price 🗺️

Use different machine learning techniques to predict house prices. You will:

  • Use Scikit-learn
  • Create neural networks with PyTorch
  • Evaluate your algorithms

Feel free to ask us any questions.

Link to every slides:

Organization


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