Deep Learning for Social Sciences

Topics

This course offers a deep dive into advanced neural network architectures and their applications, including:

• Introduction to the use of deep learning techniques in social sciences
• Basic Machine Learning concepts: regression, classification, and models
• Neural Networks: Perceptrons, Multi-Layer Perceptrons, XOR problem, activation functions, backpropagation, and gradient descent
• Convolutional Neural Networks: Image processing implementations in PyTorch
• Graph Neural Networks and their Python implementations
• Recurrent Neural Networks and Long Short Term Memory networks for time series analysis
• Generative deep learning techniques
• Advanced topics: Diffusion Models, Reinforcement Learning, and Large Language Models based on transformer architectures like the GPT series in NLP with practical implementations in PyTorch

Format

The course is structured as:

1. Lectures on theory of Deep Learning (14 sessions)
2. Coding labs (13 sessions)

Grading

The course grade is composed of four assignments delivered during the semester (40%) and a final project (60%).

Exercises

A practical part of exercises connected to the lectures will be offered. The exercises will not be graded.

Lessons