Artificial Intelligence and Machine Learning (AIML) for Connected Systems

Description:

The course intercalates theoretical lectures and lab sessions. The main idea consists of presenting the theoretical background of a specific subject, followed by a lab session in which students will learn more details about each model and algorithm with practical examples using the most popular tools and libraries available. The course includes hand-on lab sessions with practical assignments, some of which are evaluated.

The course is connected-systems oriented, which means that, in addition to the most popular datasets, like MNIST and California houses, students will also see other examples of network-related datasets.

Topics:

• Introduction to AIML.
• Practical skills and Linear Regression.
  • Lab: end-to-end work, exploratory data analysis.
• Supervised Learning and Classification (Decision Trees and Random Forest,  Bayesian Detection, Non-Parametric Classifiers)
  • Lab: Classification, Linear and Quadratic Discriminants, K-nearest neighbors (KNN).
• Dimensionality Reduction
  • Lab: Principal Component Analysis (PCA), Multiple Discriminant Analysis (MDA).
• Unsupervised Learning
  • Lab: Clustering
• Artificial Neural Networks, Deep Neural Networks (DNN)
  • Lab: Neural Networks, Multi-Layer Perceptron (MLP)
• Training enhancement techniques (e.g. Ensembles, in DNN)

complementary content:

• Bases on Reinforcement Learning
  • Lab: Bases of Reinforcement Learning
• Data processing tools (e.g. TensorFlow, Scikit learning)

Lab: management of time-series in Recurrent Neural Networks (RNN)