Machine Learning and Artificial Intelligence

Contents

1. Supervised Learning: Covers regression and classification methods including linear and logistic regression, Naive Bayes, k-Nearest Neighbors, decision trees, and Support Vector Machines.
   
2. Unsupervised Learning: Includes clustering (e.g., k-Means), dimensionality reduction (e.g., PCA, t-SNE), density estimation (e.g., Kernel Density Estimation), and anomaly detection (e.g., Isolation Forest).
   
3. Neural Networks and Deep Learning: Introduces the functioning, training, and architectures of neural networks, including Convolutional Neural Networks, Autoencoders, and generative models.
   

Additional information

1. explain and contextualize key concepts and challenges in machine learning,
   
2. understand and apply scientific methods and algorithms that operationalize these concepts, 
   
3. implement, test, and critically evaluate appropriate methods in an experimental environment, 
   
4. interpret the results in a well-reasoned manner and reflect on their ethical, societal, and technical implications.
   

Dates