Introduction to Artificial Intelligence, history and evolution of AI, types of AI (Narrow, General and Super AI), intelligent agents, problem-solving approaches, knowledge representation and reasoning, search techniques (Breadth First Search, Depth First Search and A* Search).
Expert systems, Natural Language Processing (NLP), computer vision, robotics, introduction to Generative AI and Large Language Models (LLMs), AI ethics, bias, privacy and security, and emerging trends in Artificial Intelligence.
Introduction to Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, machine learning workflow, types of machine learning (Supervised Learning, Unsupervised Learning and Reinforcement Learning), data collection and pre-processing, feature selection, training, validation and testing datasets, overfitting and under fitting, and model evaluation using accuracy, precision, recall, F1-score and confusion matrix.
Supervised learning algorithms: Linear Regression, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Naïve Bayes. Unsupervised learning algorithms: K-Means Clustering, Hierarchical Clustering and Principal Component Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh Analysis (PCA). Introduction to Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN).
Applications of Artificial Intelligence and Machine Learning in genomics, proteomics, transcriptomics, biological sequence analysis, disease diagnosis, drug discovery, protein structure prediction, precision medicine, medical image analysis, personalized medicine, AI-assisted clinical decision support systems, and ethical considerations in AI-based healthcare.
After completion of the course student will be able to:
This subject is on page 31, 32, 33 of the original AKTU PDF.
