CSE274: Applied Machine Learning Notes & Study Material
Through this course, students learn to explain types of data and data pre-processing concepts, apply feature engineering, and use dimensionality reduction techniques. They also learn to compare and implement nonlinear, regression, ensemble, and clustering models using appropriate evaluation metrics.
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Available Study Materials
Handwritten Notes & Slides
Unit-by-unit lecture summaries and handwritten notes from class toppers.
Previous Year Papers (PYQs)
LPU Mid-Term (MTE) and End-Term (ETE) questions with solutions.
Official Syllabus & CA Pattern
Course structure, credits, weightage, and continuous evaluation criteria.
Course Overview & Evaluation Scheme
CSE274 Unit-Wise Detailed Syllabus
Unit 1: Feature Engineering and Dimensionality Reduction
Detailed lecture materials, unit questions, and revision summaries available inside the Scholix portal.
Unit 2: Linear & Probabilistic Classification Models
Detailed lecture materials, unit questions, and revision summaries available inside the Scholix portal.
Unit 3: Regression
Detailed lecture materials, unit questions, and revision summaries available inside the Scholix portal.
Unit 4: Ensemble Learning
Detailed lecture materials, unit questions, and revision summaries available inside the Scholix portal.
Unit 5: Unsupervised Learning
Detailed lecture materials, unit questions, and revision summaries available inside the Scholix portal.
Unit 6: Continuous Assessment
Detailed lecture materials, unit questions, and revision summaries available inside the Scholix portal.
Frequently Asked Questions (FAQs)
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Where can I find CSE274 previous year questions (PYQs)?
Scholix maintains an extensive archive of LPU Mid-Term (MTE) and End-Term (ETE) question papers with solution guides.
What is the pass percentage & grading for CSE274?
Passing criteria follow LPU academic regulations with combined evaluation across Continuous Assessments, Attendance, and End-Term Examination.