The B.Tech in AI and ML at LPU covers a comprehensive curriculum blending computer science, mathematics, and artificial intelligence. Core topics include Programming (Python, Java, C+), Data Structures and Algorithms, Database Management, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, and Big Data Analytics. Students also study Probability and Statistics, Linear Algebra, Neural Networks, and AI ethics. The programme emphasizes practical learning through projects, internships, and lab work, preparing students for AI-driven roles in technology and research. Industry-relevant tools like TensorFlow, PyTorch,
Which topics are covered in the LPU BTech AI and ML course?
Asked by Mohit Sharma
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Hey, so LPU's BTech in AI and ML is pretty solid—covers all the essentials to get you job-ready in this booming field. Starts with basics like programming in Python, Java, data structures, algorithms, databases, and math stuff like linear algebra, probability, and stats.
Then dives into cool AI/ML topics: machine learning (supervised, unsupervised, reinforcement), deep learning, neural networks, NLP for chatbots and text stuff, computer vision for image recognition, big data analytics, and even AI ethics.
Hands-on part is awesome—labs, real projects, TensorFlow/PyTorch tools, internships, hackathons. LPU's setup with industry t...more -
Dear reader,
B.Tech at LPU is a great choice.
Both fundamental computer Science and sophisticated intelligent systems are included in the carefully crafted LPU B.Tech CSE (AI and ML) curriculum. In addition to specialised sections in Neural Networks, Deep Learning, Natural Language Processing, and Computer Vision, it covers fundamental subjects including Data Structures, Algorithms, and Database Management.
For more information kindly contact their concerned department.
I hope this information helps.
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The B.Tech in AI & ML programme at LPU begins with a very strong foundation, so you don't only learn "AI stuff," but you also comprehend the underlying technology. Electrical and Electronics Engineering, Engineering Math, Physics, Python Programming, and even practical labs like Internet Programming and Electronics Lab are covered in this course. By the time the spring term arrives, you have advanced to core computer Science courses including software engineering, discrete mathematics, database management systems, computer programming, and engineering drawing with AutoCAD. In order to help you develop holistically rather than just tech
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Answered by
Vidushi Sharma Author, research analyst, content writer, student | Contributor-Level 7
2 weeks ago...more -
In the early semesters, students study core computer-science fundamentals — including programming (Python, sometimes Java/C/C+), data structures and algorithms, databases, operating systems, computer networks, and other building-block courses like mathematics (linear algebra, probability and statistics), which are essential for ML.
As the course progresses, the AI/ML-specific modules come into play. These include Machine Learning (both supervised and unsupervised learning), Deep Learning, Natural Language Processing (NLP), Computer Vision, Expert / Intelligent Systems, and AI applications — equipping students to work on speech
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The initial semesters at LPU are constructed around fundamental engineering subjects, variable programming languages such as Python and C, data structures, algorithms, database systems, and math/statistics. Core AI/ML programs include Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Computer Vision, Reinforcement Learning, Big Data, and AI ethics. Advanced aspects include Generative AI, MLOps, optimization, and real-world project experience using tools such as TensorFlow.
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Lovely Professional University BTech AI and ML curriculum includes theory and practical subjects. Candidates also have to complete projects to gain more knowledge. The syllabus include subject list for each autumn and spring term. Mentioned below are a few topics covered in the course:
- AI and Expert Systems
- Python Programming
- Engineering Physics
- Fundamentals of Machine Learning, etc.
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