Academic year: 2021 - 2022

**ELEMENTS OF ARTIFICIAL INTELLIGENCE**

FIRST YEAR, MASTER CSI / TSAeA / PSI

ECTS: 4 credits

Classes/week: 2h Lecture; 1h Lab

Course code: 4.00/1.00/1.00

General
objective

Understanding the necessity and ways of implementing and using some artificial intelligence techniques (
machine learning, artificial neural networks – ANN, deep neural network - DNN, convolutional neural network – CNN, etc. ).

Specific objectives

» Understanding the fundamental concepts of computational intelligence/deep learning, supervised and unsupervised learning, machine learning.

» Acquiring the skills required to use ANN for function fitting, pattern recognition, prediction.

» Acquiring the skills required to use CNN for computer vision applications(image classification, object detection, pattern recognition, etc.)

» Acquiring the skills required to design and implement systems based on computational intelligence/deep learning techniques.

- Introduction. Course Presentation
- Fundamentals
- Regression
- Gradient Descent for ML / Colab notebook
- Logistic Regression and Neural Network
- Artificial Neural Network. Concepts and Paradigms
- Shallow Neural Network / Colab notebook
- Activation Functions
- Performance of a Binary Classifier
- Deep Neural Network
- CNN - Convolutional Neural Network
- CNN - Arhitectures
- CNN - Advices
- Object Detection and Localization
- Vehicle Counting using YOLO
- Recurrent Neural Networks / Bitcoin prediction application
- DNN Improvement
- Special Applications