Course Instructor: Dr. Deepti R. Bathula(Associate Professor, CSE IIT Ropar)
Computer Graphics course explores the principles and techniques used to create and manipulate visual content on computers. It covers topics such as 2D and 3D modeling, rendering, transformations, lighting, shading, and animation. Students learn how to implement algorithms for drawing shapes, handling textures, and simulating realistic scenes using frameworks like OpenGL. The course bridges the gap between theory and practical applications, enabling learners to design visually compelling graphics for gaming, simulations, and virtual reality.
Course Instructor: Dr. Deepti R. Bathula(Associate Professor, CSE IIT Ropar)
The Artificial Neural Networks (ANN) course covers the fundamentals of neural network architectures, training methods, and applications. Students learn about key concepts like perceptrons, backpropagation, and activation functions. Labs involve implementing feedforward and convolutional networks in Python, using libraries such as TensorFlow or PyTorch. Practical assignments help deepen understanding of optimization techniques, regularization, and model evaluation.
Course Instructor: Dr. Deepti R. Bathula(Associate Professor, CSE IIT Ropar)
Programming paradigms encompass a range of fundamental approaches to solving problems through code, each with its own set of principles and methodologies. These paradigms, such as procedural, object-oriented, functional, and declarative, shape how developers structure and organize their programs. On the other hand, programming pragmatics involve the practical considerations and trade-offs that developers must navigate when choosing and implementing these paradigms. This course also offered programming in various languages i.e C, C++, Java, Python, Perl etc and solving various challenging problems during labs.
Course Instructor: Dr. Deepti R. Bathula(Associate Professor, CSE IIT Ropar)
The course on digital image processing delves into the theory and application of techniques used to manipulate and analyze digital images. Students explore methods for enhancing image quality, removing noise, segmenting images into meaningful regions, and extracting valuable information. Through hands-on projects and exercises, they gain proficiency in using software tools and programming languages to implement algorithms for tasks like image filtering, edge detection, and object recognition. Additionally, the course may cover advanced topics such as image compression, image registration, and deep learning approaches to image processing. By mastering digital image processing techniques, students acquire valuable skills applicable to fields such as medical imaging, remote sensing, computer vision, and multimedia systems, among others.
Course Instructor: Dr. Sudarshan(HOD, CSE IIT Ropar)
The course on data structures and programming provides a comprehensive understanding of fundamental concepts essential for effective software development. It covers topics such as arrays, linked lists, stacks, queues, trees, and graphs, exploring their implementation, manipulation, and analysis through various algorithms. Students learn to design efficient data structures and algorithms to solve real-world problems, emphasizing principles of abstraction, encapsulation, and efficiency. Practical programming exercises and projects enable students to apply theoretical knowledge to practical scenarios, honing their problem-solving skills and algorithmic thinking. Mastery of data structures and programming equips students with essential skills for developing efficient and scalable software solutions across diverse domains, from software engineering to artificial intelligence.
Course Instructor: Dr. Shweta Jain(Assistant Professor, CSE IIT Ropar)
The data science course provides a comprehensive overview of the principles and practices essential for extracting insights and knowledge from data. Students learn a range of techniques and tools for data collection, cleaning, analysis, and interpretation. Topics covered include statistical analysis, machine learning, data visualization, and big data technologies. Through practical projects and case studies, students gain hands-on experience working with real-world datasets and applying various algorithms and methodologies to extract meaningful patterns and trends.