Introduction in Artificial Intelligence

COURSE DESCRIPTION

This course provides an extensive overview of Artificial Intelligence (AI) and Machine Learning (ML), focusing on their theoretical and practical aspects. It is designed to equip students with a deep understanding of AI and ML, emphasizing their application in problem-solving scenarios using Python programming language. The course begins with an introduction to the fundamentals of AI, including its historical context and prospects, and progresses to explore Python's role as a critical tool in AI and ML applications.

CONTENT

1. Introduction to Artificial Intelligence
2. Introduction to Python and Frameworks
3. Search Algorithms (Timsort, Bubble Sort, DFS, BFS, Dijkstra, A*)
4. Knowledge Databases (Logic, Ontologies, Graphs, Concept Vector Databases)
5. Decision Making (Fuzzy Logic, ID3, Random Forest)
6. Data Dimension Reduction Solutions (PCA, t-SNE, UMAP)
7. Clustering Algorithms (k-MEANS, SpectralClustering, DBSCAN)
8. Classification and Regression (SVM, GMM)
9. Optimization Algorithms (GA, MC, PSO, Bayesian Process)
10. Regression and Classification Solutions based on Artificial Neural Networks (SGD, PyTorch, TensorFlow)
11. Computer Vision Solutions (OpenCV, ConvNet, ResNet, DenseNet, Yolo)
12 Unsupervised Machine Learning (AE, VAE, DML)
13. Time Series Models (RNN, LSTM, Transformer T5)
14. Large Language Models (Query Engineering, ChatGPT, LLM, LLaMA)
15. Generative Models (GAN, Diffusion)
16. Reinforced Learning (DQN, PPO)