GradePack

    • Home
    • Blog
Skip to content
bg
bg
bg
bg

GradePack

Q14: (4 points)What is the strategy of AdaBoosting for rewei…

Q14: (4 points)What is the strategy of AdaBoosting for reweighing the training data points?

Read Details

Q33: (8 points)After taking the action suggested in the prev…

Q33: (8 points)After taking the action suggested in the previous question, suppose the discount factor is γ = 0.9, the state transfers from s₂ to s₄ after taking action aₜ, and the reward r is 0.6. Please update the Q-table and write down the updated Q-table. Note: Only one value in the table needs updating, and you might need the Bellman Equation:

Read Details

Q13: (3 points)In AdaBoosting, there are 8 data instances (i…

Q13: (3 points)In AdaBoosting, there are 8 data instances (i.e., samples). Before updating the weights of the eight samples, the weights of the eight samples are[sample1: 1/8, sample2: 1/8, sample3: 1/8, sample4: 1/8, sample5: 1/8, sample6: 1/8, sample7: 1/8, sample8: 1/8]. After updating the weights of the eight samples, the new weights of the eight samples are[sample1: 0.07, sample2: 0.07, sample3: 0.51, sample4: 0.07, sample5: 0.07, sample6: 0.07, sample7: 0.07, sample8: 0.07]. Which data instances (samples) is/are correctly classified by the decision stump of this iteration?

Read Details

Q34: (15 points) What are the two key steps of value learn…

Q34: (15 points) What are the two key steps of value learning? (4 points) What are the two key steps of policy gradient? (4 points) If we are to build a reinforcement learning with discrete actions, which method we should use? (2 points) What are the weaknesses of value learning (e.g., DQN)? (5 points)

Read Details

Q28: (6 points)What are the three loss function names of Ran…

Q28: (6 points)What are the three loss function names of RankSVM, RankBoost, and RankNet discussed in the lectures?

Read Details

Q25: (10 points) Item-Item Collaborative FilteringBelow is u…

Q25: (10 points) Item-Item Collaborative FilteringBelow is user-movie rating matrix with partial ratings available. Please use the item-item collaborative filtering method to estimate the rating of the user #5 for the movie #1. Hints: first use Pearson correlation as similarity by: subtracting mean rating from each movie, then calculating cosine similarities (Sij) between rows. Later, we predict the rating by taking weighted average using the equation: rix=∑j∈N(i;x)Sij⋅rjx∑Sijr_{ix} = \frac{\sum_{j \in N(i;x)} S_{ij} \cdot r_{jx}}{\sum S_{ij}}​​ where i is the index of an item, x is the index of a user, rjxr_{jx} is the rating of the user x for the item j, N(i;x)N(i;x) is the selected neighbor set of the item i given the user x, and the number of the neighbor set (∣N(i;x)∣|N(i;x)|) is 2.

Read Details

Q12: (3 points) Answer True or False only: In DQN, the Q va…

Q12: (3 points) Answer True or False only: In DQN, the Q value function is instantiated by a neural network.

Read Details

Evaluate the role of the United Nations in global geopolitic…

Evaluate the role of the United Nations in global geopolitics post-World War II. What challenges has it faced in its mission to promote international cooperation and peace?

Read Details

Which event marked a significant confrontation between capit…

Which event marked a significant confrontation between capitalism and communism during the Cold War?

Read Details

The Industrial Revolution began in the United States before…

The Industrial Revolution began in the United States before spreading to Europe.

Read Details

Posts pagination

Newer posts 1 … 25,335 25,336 25,337 25,338 25,339 … 95,695 Older posts

GradePack

  • Privacy Policy
  • Terms of Service
Top