This repository contains all the assignments for the AI course "Applied Artificial Intelligence" offered at McMaster University. Each assignment is designed to reinforce the concepts learned in class and provide hands-on experience with various machine learning techniques.
This course aims at equipping students with practical skills to develop real-world AI applications across various sectors while emphasizing ethical considerations and foundational concepts in modern Artificial Intelligence.
"This course covers the principles of modern Artificial Intelligence (AI) in a practical, hands-on way. Students will build practical, real-world AI-powered applications across a diverse range of sectors, including autonomous vehicles, business analytics, energy systems, financial technologies, and healthcare. Concepts such as Generative AI (GenAI), Natural Language Processing (NLP), Deep Learning (DL), recommendation engines, and computer vision will be covered. The course also covers AI ethics and key ingredients to build responsible AI systems."
The course description provided in this repository is sourced from: Applied Artificial Intelligence Course Outline
Each folder contains the code for the assignemnent as a jupyter notebook and a README.md file. The README.md aims at providing explainations about the assignement and provide the link to the dataset used if available. Datasets will not be shared directly in this repository.
There are three assignments, each building upon concepts covered in the lectures. For more detailed information, please refer to the respective README files for each assignment.
- Data Preprocessing, Tabular Data Analysis, Regression models (Linear Regression, Random Forest etc.)
- Convolutional Neural Networks for MultiClass Classification
- Fine Tuning for Image Classification on the CIFAR10 Dataset
The assignments were done inside a conda environment with Tensorflow 2.13.0.