LPU - Lovely Professional University
What you'll learn

This course provides a comprehensive introduction to Artificial Intelligence, covering the journey from foundational AI concepts to advanced topics such as Machine Learning, Deep Learning, Generative AI, and Agentic AI. Students will explore intelligent systems, neural networks, supervised and unsupervised learning techniques, and modern AI applications used in healthcare, business, robotics, and automation. The course emphasizes both theoretical understanding and practical implementation using modern AI tools and frameworks, preparing learners for emerging AI-driven technologies and real-world problem solving. After successful completion of the course, learners will be able to: 1. Understand the fundamental concepts and applications of Artificial Intelligence. 2. Apply neural networks and soft computing techniques to solve computational problems. 3. Build and evaluate supervised and unsupervised machine learning models. 4. Develop basic deep learning applications for image, text, and data analysis. 5. Understand the principles of Generative AI and Agentic AI systems. 6. Design intelligent AI-based solutions for real-world applications using modern AI tools and frameworks.

  • • Introduction to AI and its history • Types and applications of AI • Intelligent Agents and Problem Solving • Search Techniques: BFS, DFS, Heuristic Search • Knowledge Representation Basics • Ethics and Challenges in AI
  • • Introduction to Neural Networks • Artificial Neuron and Perceptron • Multi-Layer Perceptron and Backpropagation • Activation Functions • Introduction to Soft Computing • Fuzzy Logic • Genetic Algorithms • Swarm Intelligence: o Ant Colony Optimization (ACO) o Particle Swarm Optimization (PSO)
  • • Introduction to Generative AI • Large Language Models (LLMs) • Transformers and Prompt Engineering • AI Chatbots and Virtual Assistants • Introduction to Agentic AI • AI Agents, Planning, Memory, and Tool Usage • Multi-Agent Systems • Applications and Future Trends of Agentic AI
  • • Introduction to Machine Learning • Types of Machine Learning • Data Preprocessing • Regression and Classification • Linear Regression • Logistic Regression • Decision Trees and Random Forest • Support Vector Machine (SVM) • K-Nearest Neighbors (KNN) • Model Evaluation Metrics
  • • Introduction to Unsupervised Learning • Clustering Techniques: o K-Means o Hierarchical Clustering • Dimensionality Reduction using PCA • Introduction to Deep Learning • Deep Neural Networks (DNN) • Convolutional Neural Networks (CNN) • Recurrent Neural Networks (RNN) and LSTM • Applications in Computer Vision and NLP

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Dr. Nikita
Assistant Professor

Dr. Nikita Singla is an experienced academician in the field of Computer Science and Engineering with over 8 years of teaching and research experience. She has also conducted numerous placement preparation sessions to guide students toward career success. She has published more than 30 research papers in reputed Scopus-indexed journals and conference proceedings, reflecting her strong contribution to the research community. Dr. Singla has actively guided numerous B.Tech, M.Tech, and Ph.D. students in their academic and research pursuits. She is SQL certified and possesses hands-on expertise in Artificial Intelligence and Machine Learning too, with a focus on practical and research-driven learning.


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Dr. Jimmy Singla
Professor

Dr. Jimmy Singla is currently a Professor with the Department of Computer Science and Engineering, Lovely Professional University, Punjab. He is an academician with more than twelve years of experience in teaching, research, and development. He has more than 90 research publications in various reputed conferences and journals. His research interests include expert systems, machine learning, software engineering, and intelligent systems.