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35. Explain the scale-invariant feature transformation algorithm in detail.
Advanced Computer Vision
Medium
36. Explain Edge detection in detail. What are the most commonly used edge detection methods?
Computer Vision
37. Explain Central Limit Theorem in detail.
Machine Learning Basics, Probability
Easy
38. What is a P-value, and how to interpret them?
39. What are Type I and Type II errors? How to avoid them?
Machine Learning Basics
40. What is object tracking? What are some standard techniques to do object tracking?
41. What is Image Segmentation? What's the difference between instance segmentation and semantic segmentation?
42. Describe how gradient boosting works.
Advanced Machine Learning
43. Write a function to make a biased coin from a fair coin?
Probability
44. Given a generator of unbiased Bernoulli numbers (0 or 1 with p=0.5), create a biased Bernoulli trial generator (generate 0 or 1 with the specified 0 < p < 1)