100 Machine Learning PhD viva Questions

Are you preparing for your Machine Learning PhD viva and feeling overwhelmed by the thought of the questions you might be asked? You’re not alone. The viva, or oral examination, is a crucial part of the PhD process, and it can be challenging to know how to prepare for it.

Fortunately, we’ve got you covered. In this article, we’ve compiled a list of 100 Machine Learning PhD viva questions that are likely to come up during your examination. By reviewing these questions and preparing thoughtful answers, you can boost your confidence and improve your chances of success.

100 Machine Learning PhD Viva Questions: A Comprehensive Guide for Defending Your Thesis

1. What is your research topic, and what motivated you to pursue it?
2. What are the key research questions you aimed to answer in your PhD project?
3. What is the main contribution of your research to the field of Machine Learning?
4. Can you explain the technical approach you used to tackle your research problem?
5. What are the limitations of your research, and how did you address them?
6. What are the potential applications of your research in industry or academia?
7. How does your work fit into the broader context of the Machine Learning field?
8. What were the key challenges you faced during your research, and how did you overcome them?
9. Can you explain the methodology you used to collect and analyze your data?
10. What are the key insights you gained from your research, and how do they contribute to the field?
11. What are the main strengths and weaknesses of the Machine Learning methods you used in your research?
12. Can you explain the differences between supervised and unsupervised learning, and when to use each method?
13. What are the key steps in building a successful Machine Learning model?
14. What are the advantages and disadvantages of using deep learning models?
15. How do you handle overfitting in your models, and what techniques do you use to prevent it?
16. What is regularization, and how does it help prevent overfitting?
17. How do you handle missing data in your Machine Learning models?
18. What is cross-validation, and how does it help evaluate the performance of your models?
19. Can you explain the difference between precision and recall, and why they are important metrics in Machine Learning?
20. What is the curse of dimensionality, and how does it affect the performance of Machine Learning models?
21. Can you explain the difference between a generative and a discriminative model?
22. What are the advantages and disadvantages of using decision trees for Machine Learning?
23. Can you explain the difference between gradient descent and stochastic gradient descent?
24. What is backpropagation, and how does it work in training neural networks?
25. What are convolutional neural networks, and how are they used in computer vision?
26. Can you explain the difference between a convolutional layer and a pooling layer in a CNN?
27. What are recurrent neural networks, and how are they used in natural language processing?
28. Can you explain the difference between a LSTM and a GRU?
29. What are autoencoders, and how are they used in unsupervised learning?
30. What is transfer learning, and how can it be used to improve the performance of Machine Learning models?
31. What are adversarial attacks, and how do they affect the robustness of Machine Learning models?
32. What is reinforcement learning, and how does it differ from supervised and unsupervised learning?
33. Can you explain the difference between on-policy and off-policy learning in reinforcement learning?
34. What is Q-learning, and how does it work in reinforcement learning?
35. What are the challenges in scaling Machine Learning models, and how can they be addressed?
36. How do you evaluate the performance of a Machine Learning model, and what metrics do you use?
37. Can you explain the difference between accuracy and F1 score, and when to use each metric?
38. How do you interpret the results of a confusion matrix, and what insights can you gain from it?
39. What are the different techniques for feature selection, and how do you decide which one to use?
40. Can you explain the difference between feature extraction and feature selection, and when to use each technique?
41. What are the different types of clustering algorithms, and how do they differ from each other?
42. Can you explain the difference between K-means clustering and hierarchical clustering?
43. What are the challenges in clustering high-dimensional data, and how can they be addressed?
44. What are the different types of classification algorithms, and how do they differ from each other?
45. Can you explain the difference between logistic regression and support vector machines?
46. What are the different types of ensemble learning methods, and how do they work?
47. Can you explain the difference between bagging and boosting?
48. What are the advantages and disadvantages of using random forests for classification tasks?
49. What are the different types of time-series forecasting models, and how do they differ from each other?
50. Can you explain the difference between ARIMA and exponential smoothing models?
51. What are the different types of anomaly detection algorithms, and how do they work?
52. Can you explain the difference between unsupervised and semi-supervised anomaly detection?
53. What are the different types of deep learning architectures, and how do they differ from each other?
54. Can you explain the difference between a feedforward neural network and a convolutional neural network?
55. What are the different types of optimization algorithms, and how do they differ from each other?
56. Can you explain the difference between batch gradient descent and mini-batch gradient descent?
57. What are the different regularization techniques, and how do they prevent overfitting?
58. Can you explain the difference between L1 and L2 regularization?
59. What are the different types of data augmentation techniques, and how do they improve the performance of Machine Learning models?
60. Can you explain the difference between data augmentation and transfer learning?
61. What are the different types of hyperparameter tuning techniques, and how do they help optimize Machine Learning models?
62. Can you explain the difference between grid search and random search?
63. What are the different types of model interpretability techniques, and how do they help understand the inner workings of Machine Learning models?
64. Can you explain the difference between LIME and SHAP?
65. What are the ethical considerations in Machine Learning, and how do you ensure that your research is ethically sound?
66. Can you explain the difference between bias and variance in Machine Learning, and how do you balance them?
67. What are the challenges in building Machine Learning models for real-world applications, and how do you address them?
68. Can you explain the difference between batch learning and online learning, and when to use each method?
69. What are the different types of data preprocessing techniques, and how do they prepare data for Machine Learning?
70. Can you explain the difference between normalization and standardization?
71. What are the different types of distance metrics, and how do they measure similarity between data points?
72. What are the different types of optimization problems in Machine Learning, and how do you solve them?
73. Can you explain the difference between convex and non-convex optimization problems?
74. What are the different types of regularization techniques, and how do they prevent overfitting?
75. Can you explain the difference between dropout and weight decay regularization?
76. What are the different types of learning rate schedules, and how do they impact the training of Machine Learning models?
77. Can you explain the difference between a fixed learning rate and an adaptive learning rate?
78. What are the different types of data imbalance, and how do you handle them in Machine Learning?
79. Can you explain the difference between oversampling and undersampling?
80. What are the different types of neural network architectures for sequence modeling, and how do they differ from each other?
81. Can you explain the difference between a Recurrent Neural Network and a Transformer?
82. What are the different types of attention mechanisms, and how do they improve the performance of Machine Learning models?
83. What are the challenges in building Machine Learning models for natural language processing, and how do you address them?
84. Can you explain the difference between word embeddings and character embeddings?
85. What are the different types of text classification algorithms, and how do they differ from each other?
86. What are the different types of named entity recognition algorithms, and how do they work?
87. What are the different types of sentiment analysis algorithms, and how do they determine the sentiment of a piece of text?
88. What are the different types of topic modeling algorithms, and how do they discover topics in a corpus of text?
89. What are the different types of Machine Learning models for image classification, and how do they differ from each other?
90. Can you explain the difference between a Convolutional Neural Network and a Residual Neural Network?
91. What are the different types of object detection algorithms, and how do they detect objects in an image?
92. What are the different types of image segmentation algorithms, and how do they segment an image into regions?
93. What are the different types of generative models, and how do they generate new data samples?
94. Can you explain the difference between a Generative Adversarial Network and a Variational Autoencoder?
95. What are the different types of Machine Learning models for speech recognition, and how do they differ from each other?
96. Can you explain the difference between a Hidden Markov Model and a Deep Neural Network for speech recognition?
97. What are the different types of Machine Learning models for time-series forecasting, and how do they differ from each other?
98. Can you explain the difference between a recurrent neural network and a convolutional neural network for time-series forecasting?
99. What are the different types of Machine Learning models for recommender systems, and how do they recommend items to users?
100. Can you explain the difference between collaborative filtering and content-based filtering for recommender systems?

These 100 Machine Learning PhD viva questions cover a wide range of topics and concepts in Machine Learning, from basic concepts such as linear regression and decision trees, to more advanced topics such as deep learning, natural language processing, and image recognition.

By preparing for these questions, you will be well-equipped to defend your thesis and demonstrate your knowledge and expertise in the field of Machine Learning. Remember to keep practicing and honing your skills, as the field of Machine Learning is constantly evolving and there is always something new to learn. Good luck with your viva!

Also Read: Top 50 Possible PhD Viva Questions