﻿ 机器学习考试代考 CSE 158代写 – 天才代写

# 机器学习考试代考 CSE 158代写

2022-06-11 11:13 星期六 所属： 机器学习代写 浏览：405

## CSE 158: Midterm

### Instructions

Do not open or start the test before instructed to do so.

Note that the final page contains some algorithms and definitions. Total marks = 26

### Section 1: Regression and Ranking (6 marks)

In this section we’ll consider building predictors from (features extracted from) movies in the Marvel Cinematic Universe. A sample of such a datasets looks like the following:

(CA = Captain America; IM = Iron Man; BP = Black Panther; SW = Scarlet Witch; AM = Ant-Man; SM = Spider Man; DS = Doctor Strange; T = Thor; H = Hulk)

#### 1.

Suppose you want to train a predictor of the form

Rating = θ0 + θ1 · [number of heroes] + θ2 · [budget]

Write down the feature representations of the first four datapoints in the space below (1 mark):

Rating = __ × θ

#### 2.

Suppose you wanted to use the ‘lists of heroes’ and ‘release dates’ as features to predict the rating. Describe feature representations you might use for each, and write down your representations for the first two movies (2 marks):

A:

#### 3.   机器学习考试代考

Describe two additional features (possibly outside of those listed in the table above) that might be useful for predicting the rating (2 marks):

A:

#### 4.

You train two predictors to predict the rating, that yield the following results:

Predictor 1: Rating = 90 + 1.1 · [number of heroes] 2.8 × 1011 · [budget]

Predictor 2: Rating = 90 + 1.3 × 108 · [budget]

Give a potential explanation as to why the coefficient associated with ‘budget’ is much larger in the second predictor compared to the first (1 mark):

A:

### Section 2: Classification and Diagnostics (9 marks)   机器学习考试代考

Diagnose one potential problem with each of the following experimental pipelines (there could be more than one problem, but it is sufficient to identify a single issue), and suggest a potential correction:

#### 5.

You train a binary classifier based on words in a document to distinguish positive versus negative sentiment. You use a 1,000 word dictionary (i.e., 1,001 featuresincluding the offset). You collect 2,000 samples, and withhold half for testing. Around half of the labels are positive. Your method has 98% accuracy on the training set but is no better than random on the test set (2 marks).

Problem:

Solution:

#### 6.

Using a large dataset, you train a regressor that has an R2 coefficient of 0.3 on the training set, but R2 = 0.2 on the test set (2 marks).

Problem:

Solution:

#### 7.   机器学习考试代考

Using a large dataset, you train a content filter (a classifier) to detect R-rated (i.e., adult) novels among a corpus of 100,000 books, based on their descriptions. Your classifier has approximately 98% accuracy on both the training and test sets, but fails to identify any R-rated novels (2 marks).

Problem:

Solution:

Design an appropriate measure of “success” for each of the following situations. Your measure could be a known metric (accuracy, BER, etc.), a combination of metrics, or a new metric that you design specifically for the task.

#### 8.

You want to filter spam e-mails (i.e., build a classifier that identifies spam). It is okay to let a few spam e-mails through the filter, but the number of non-spam e-mailsmistakenly filtered should be close to zero (1 mark).

A:

#### 9.

You want to train a regressor to predict sentiment scores (e.g. ratings). However your dataset consists of 95% female users and 5% male users, and you want your regressor to perform about equally well for both groups (1 mark).

A:

#### 10.

You want to build a search engine to find songs based on partially-remembered lyrics (i.e., a user enters some lyrics in the search bar, and you return a ranked list of results via a UI). You know there is exactly one relevant result for each query (i.e., the user is searching for one specific song) (1 mark).

A:

### Clustering / Communities (5 marks)  机器学习考试代考

Suppose you have a dataset from a social restaurant review network (such as Yelp) which contains both home addresses of different users, as well as their social networks. That is, you have 2-d data representing their latitude and longitude coordinates, as well as an adjacency matrix representing users’ social connections.

#### 11.

Among the following algorithms, which of them would potentially be good choices to build feature representations? Assume your goal is to predict which restaurant a user will visit next. For any that are not useful, explain in a few words (3 marks):

(a) PCA / (b) K-means / (c) Hierarchical Clustering / (d) Graph Cuts / (e) Clique Percolation / (f) Connected

Components

Useful:

Not useful:

#### 12.

Dimensionality reduction algorithms (e.g. clustering and community detection) typically have parameters (number of dimensions, number of clusters etc.) that control their output. Briefly describe how you would select these parameters for the algorithms you chose above through the use of a training and validation set. That is, describe the pipeline you would use to build a predictor that incorporated one or more dimensionality reduction algorithms (2 marks):

A:

### Recommender Systems (6 marks)  机器学习考试代考

#### 13.

Suppose a user listens to a sequence of songs on an online streaming service. The only feedback they can provide is ‘thumbs up’/‘thumbs down,’ (which can be 1, -1, or missing) as well as implicit feedback in the form of finishing or skipping a track. Each time a track finishes (or is skipped), you must select a new track that you expect the user to like. E.g. sequences for two users might look like:

Describe what algorithms you would use to select the next song, and what comparisons you would make (e.g. if using a set similarity metric, what sets would be used as inputs?) (3 marks):

A:

#### 14.

(Design thinking) Suppose you want to build a system to recommend running routes to users based on historical data about their exercises (e.g. GPS and heartrate data extracted from a smartwatch, and other metadata).

Describe what features you would use, what algorithms you would select, how you would measure performance (etc.) in order to build a recommendation pipeline from this data (3 marks).

A: