Machine Learning

CT-1 CSE 431 Machine Learning (Sec-C) Total Marks-15 Time-40Mins 1. A university wants to predict whether a student will pass (1) or fail (0) a final examination based on two factors: Study Hours per Week and Attendance Percentage. The following training data are collected: Student | Study Hours (X1) | Attendance % (X2) | Pass (Y) S1 | 2 | 55 | 0 S2 | 3 | 60 | 0 S3 | 4 | 65 | 0 S4 | 5 | 70 | 1 S5 | 6 | 75 | 1 S6 | 7 | 80 | 1 S7 | 8 | 85 | 1 S8 | 9 | 90 | 1 Calculate the value of z and the probability of passing for a student who studies 4 hours/week and has 65% attendance. Using a classification threshold of 0.5, determine whether the student is predicted to Pass or Fail. Calculate the probability of passing for a student who studies 7 hours/week and has 80% attendance. [10] 2. Define Outcome-Based Education (OBE). 3. Differentiate between Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs). 4. A student obtains 6 marks out of 12 marks in assessment items mapped to CO1. Calculate the CO1 attainment percentage. Determine whether CO1 is attained if the attainment threshold is 50%. 5. What is a Complex Engineering Problem (CEP)? 6. List the six cognitive levels of Bloom's Taxonomy in order from lower-order