Machine Learning for Data Science (AL-702 (D)) - Important Questions

Explain how the randomization technique can improve algorithm performance.

Introduce algorithms. Write down characteristics of algorithms.

Explain dynamic programming with a suitable example and discuss its applications in machine learning and personal genomics.

What is the role of hash tables in search algorithms?

Discuss use of machine learning in the field of graphs, maps and map searching.

Explain the concept of NP completeness in the context of algorithms.

Describe the significance of data science in analyzing massive genomic data.

Explain the introduction to machine learning, its types and applications in data science with suitable examples.

Write a detailed note on cross-validation and holdout method and explain their role in model evaluation and accuracy.

What is ensemble learning? Explain its advantages in predictive modeling.

Define linear classification and probabilistic modeling in machine learning.

Explain how model selection is essential for machine learning performance.

Discuss the concept, need and usage of probabilistic inference in machine learning / data science.

Discuss the concept of data description and preparation including data cleaning and transformation in machine learning applications.

What are topic models and how are they applied in data analysis?

Discuss the concept of prediction of preterm birth under the machine learning applications.