Important Questions (Unit-3, 4, 5) Rajasthan Technical University, Kota 2019-20 6CS4-02: MACHINE LEARNING
Important Questions (Unit-3, 4, 5) Rajasthan Technical University, Kota 2019-20 6CS4-02: MACHINE LEARNING PART A (1) (2) Define precptron in Artificial neural network. Name any five parameter to Evaluate Machine Learning algorithms. (3) Write equation for Q-learning. (4) What is the need of Recommendation system ? (5) What is the difference between machine learning and deep learning ? (6) How reinforcement learning is different from machine learning ? (7) How EM algorithm is different from k-means algorithm ? (8) What is the need of activation function ? (9) (10) What do you mean by backpropagation in Artificial neural network ? What is roll of hidden layers in Artificial neural network ? PART B (1) (2) (3) (4) (5) (6) Differentiate between Model-based and Model-Free Reinforcement learning. How Feature extraction is different from Feature selection, exlain with a suitable example ? Write Short notes on: MDP and SVD. Explain Q-learning reinforcement learning with a suitable example. What is the importance of features in any machine learning algorithm ? Explain Feature selection with a suitable example. Explain the types of Deep learning models. PART C (1) (2) (3) (4) What do you mean by Principal component analysis ? How to find a PCA, take any dataset and explain the method ? What do you mean by reinforment learning, Explain in detail. What is the need of Policy iteration and Value iteration? Explain all the aspects of Artifiical nural network with its application. Write short notes on: a) Bellman equations b) Policy evaluation using Monte Carlo c) Confusion matrix (include all the parameters) d) Collaborative filtering and Content-based filtering
Important Questions (Unit-3, 4, 5) Rajasthan Technical University, Kota 2019-20 6CS4-02: MACHINE LEARNING
- 1. Page 1 of 1 Important Questions (Unit-3, 4, 5) Rajasthan Technical University, Kota 2019-20 6CS4-02: MACHINE LEARNING PART A (1) (2) Define precptron in Artificial neural network. Name any five parameter to Evaluate Machine Learning algorithms. (3) Write equation for Q-learning. (4) What is the need of Recommendation system ? (5) What is the difference between machine learning and deep learning ? (6) How reinforcement learning is different from machine learning ? (7) How EM algorithm is different from k-means algorithm ? (8) What is the need of activation function ? (9) (10) What do you mean by backpropagation in Artificial neural network ? What is roll of hidden layers in Artificial neural network ? PART B (1) (2) (3) (4) (5) (6) Differentiate between Model-based and Model-Free Reinforcement learning. How Feature extraction is different from Feature selection, exlain with a suitable example ? Write Short notes on: MDP and SVD. Explain Q-learning reinforcement learning with a suitable example. What is the importance of features in any machine learning algorithm ? Explain Feature selection with a suitable example. Explain the types of Deep learning models. PART C (1) (2) (3) (4) What do you mean by Principal component analysis ? How to find a PCA, take any dataset and explain the method ? What do you mean by reinforment learning, Explain in detail. What is the need of Policy iteration and Value iteration? Explain all the aspects of Artifiical nural network with its application. Write short notes on: a) Bellman equations b) Policy evaluation using Monte Carlo c) Confusion matrix (include all the parameters) d) Collaborative filtering and Content-based filtering