Document Classification: Transformers and MLPs

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Explores Georges Perec's innovative literary style and his fascination with everyday life, urban spaces, and human existence.

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Document Classification

Explores document classification methods, including Naïve Bayes and word embeddings.

Music and Emotion: Understanding Expressive Communication

Explores the communication differences between music and language, models of emotion, and how emotions can be measured in music.

Textual Data Analysis: Classification & Dimensionality Reduction

Explores textual data classification, focusing on methods like Naive Bayes and dimensionality reduction techniques like Principal Component Analysis.

Convolutional Neural Networks

Covers Convolutional Neural Networks, including layers, training strategies, standard architectures, tasks like semantic segmentation, and deep learning tricks.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Advanced Physics I: Introduction to Mechanics

Introduces the basics of physics, including mechanics and making predictions based on observations and hypotheses.

Graphical Models: Probability Distributions and Factor Graphs

Covers graphical models for probability distributions and factor graphs representation.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.