# |
Question |
Topic |

Q1 |
What are the differences between classification and clustering? |
Week1 |

Q2 |
In which cases is the normal equation good for a linear regression problem (in terms of dimension & size of data)? |
Week2 |

Q3 |
Why do we use different types of error functions: Mean Square Error and Cross Entropy Error? |
Week2 |

Q4 |
Discuss the relation between mixing coefficient (𝛑) and responsibility (γ) in a GMM model. |
Week3 |

Q5 |
Discuss the relation between GMM and HMM based on their corresponding parameters. |
Week4 |

Q6 |
Why do we introduce the marginalization out (z_{{k-1}}) in the derivation of the solution for viterbi decoding algorithm? |
Week4 |

Q7 |
What is the phytiscal meaning of lagrange multiplers shown in the dual problem of SVM? |
Week5 |

Q8 |
Kernel trick: why is it called a trick? |
Week5 |

Q9 |
What is the difference between X^{T}X and the covariance matrix from cov(X)? |
Week6 |

Q10 |
Explain the relation between prinicipal components and eign values/vectors of the covariance matrix from cov(X). |
Week6 |

Q11 |
Explain why cross entropy error function goes well with Softmax activation function. |
Week7 |

Q12 |
Prove the partial derivative shown in slide 33 (it is related to Q11). |
Week8 |

Q13 |
What do 1) epoch and 2) batch mean in the training of a machine learning model? |
Week9 |

Q14 |
Explain the difference between gradient descent (GD) and stochastic gradient descent (SGD). |
Week9 |

Q15 |
Why is LSTM robust against gradient vanishing problem compared to vanilla RNN? |
Week10 |

Q16 |
Explain what dropout is and how it can be applied in RNN. |
Week11 |

Q17 |
Discuss pros and cons of both VAE and GAN models. |
Week12 |

Q18 |
What is Batch Normalization and why do we use it? |
Week13 |

Q19 |
Explain why action value fucntion is preferable to state value function in RL. |
Week14 |

Q20 |
How does experience replay help the convergence of DQN? |
Week14 |

Q21 |
How does entropy regularization help the exploration in PG based RL algorithms? |
Week15 |

Q22 |
Your comments on this course for its future improvement. |
PML |