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.

With clear explanation describe the Tools to analyze algorithms.

Describe in detail about the concept of Divide and Conquer technique with the help of a related example.

What is the role of hash tables in search algorithms?

Discuss the applications of dynamic programming in personal genomics.

Discuss Use of Machine Learning in the field of Graphs, Maps and Map searching.

Discuss application of Stable marriages algorithms in the field of Machine Learning.

Describe the importance of the dynamic programming algorithm with a related example in machine learning.

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

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

Explain the concept of Interconnectedness on Personal Genomes in Machine Learning.

Describe the main types of machine learning algorithms and their applications in data science.

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

Describe the steps involved in cross-validation and its role in model accuracy.

Define linear classification and probabilistic modeling in machine learning.

Explain how model selection is essential for machine learning performance.

What is the gradient descent delta rule? Explain with an example.

Define a well-posed learning problem and explain it with an example.

Write a detailed note on the Holdout method in machine learning.

Discuss the least squared error hypothesis in machine learning.

Describe lazy versus eager learning with an example.

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

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

Explain the importance of data cleaning in data preprocessing.

Discuss how data transformation affects model building in machine learning.

Explain Probabilistic modelling using an example under the Machine Learning applications.

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

Discuss the concept of prediction of preterm birth under the Machine Learning applications.

Define OLAP and OLTP and their applications in data warehousing.

Explain the connection between data warehousing and data mining in data science.