LPU - Lovely Professional University
What you'll learn

This interdisciplinary course is designed to introduce students from all engineering backgrounds to the foundational concepts and practical applications of Artificial Intelligence (AI) and Machine Learning (ML). Through hands-on activities, real-world datasets, and mini-projects, students will learn how intelligent systems are built and how they can be used to solve real-life engineering and societal problems. The course emphasizes problem-solving, data handling, model building, and interpretation of results, making it accessible even for students with no prior programming or AI/ML background. Whether you're from Civil, Mechanical, Electrical, Biotechnology, or Computer Science, the course will provide domain-relevant examples and applications tailored to your field.

  • "Introduction to AI and ML: Definitions, History, and Applications, Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning, Real-World Examples of AI in Daily Life and Industry, Understanding AI Project Lifecycle, Introduction to Python Setting up Python Environment.
  • "Linear Algebra: Vectors, Matrices, Dot Products, Probability: Random Variables, Distributions, Bayes' Theorem ,Statistics: Mean, Variance, Correlation, Covariance ,Optimization: Gradient Descent, Cost Functions ,Python for ML: NumPy, Matplotlib Basics
  • Data Types, Collection Methods, and Data Quality Issues ,Data Cleaning, Normalization, Handling Missing Values ,Feature Engineering and Encoding Techniques ,Train-Test Split, Cross-Validation, and Bias-Variance Tradeoff ,Linear and Logistic Regression: Theory + Hands-on
  • Decision Trees: Concepts and Implementation, Random Forests: Concepts and Implementation ,Support Vector Machines: Kernels and Applications ,KNN and Naive Bayes Classifiers ,Model Evaluation Metrics: Accuracy, Precision, Recall, F1, AUC-ROC
  • Clustering: K-Means,Hierarchical Clustering ,Dimensionality Reduction: PCA and Applications ,Introduction to Neural Networks: Perceptron, Activation Functions ,Training Deep Neural Networks and Backpropagation ,CNNs and RNNs Introduction: Image and Sequence Processing
  • AI in Healthcare, Agriculture, Finance, and Smart Cities, Real-World Case Studies: Disease Prediction, Yield Forecast, Traffic Analytics ,TensorFlow and Keras: Basic Model Building & Training

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Ajmer Singh
Assistant Professor

Mr. Ajmer Singh is an experienced academician at Lovely Professional University (LPU) with a strong background in teaching Electronics, Embedded Systems, Digital Design, Python programming, Artificial Intelligence and Machine Learning. He is committed to delivering high-impact technical education through a hands-on, learner-centric approach. His pedagogy emphasizes bridging theoretical concepts with practical implementation, extending from circuit analysis and simulation environments to data-driven modeling, intelligent systems, and real-world AI applications using modern tools and frameworks.


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Dr. Rosepreet Kaur Bhogal
Associate Professor

Dr. Rosepreet Kaur Bhogal has over 15 years of academic and training experience in the fields of Python programming, Artificial Intelligence, Machine Learning, and Signal Processing. She has successfully conducted various skill development programs, workshops, and technical training sessions aimed at enhancing students’ practical and industry-relevant competencies. Her teaching methodology blends conceptual clarity with hands-on implementation, empowering learners to solve real-world problems through coding, automation, and analytical thinking. Her experience also includes mentoring student projects, supporting placements, and contributing to research and innovation initiatives at the university level.