154.In the process of preparing department income statements, a company uses there are three steps before the statements can be completed. Describe those steps. 155.What is the cycle time for a manufacturer? What does it reveal about the manufacturing process? 156.Riu Corporation has a Parts Division that does work for other Divisions in the company as well as for outside […]
21.During its first year of operations, Silverman Company paid $14,000 for direct materials and $19,000 for production workers’ wages. Lease payments and utilities on the production facilities amounted to $17,000 while general, selling, and administrative expenses totaled $8,000. The company produced 5,000 units and sold 3,000 units at a price of $15.00 a unit.What is Silverman’s cost of goods sold for the year? […]
51.Which of the following is human resource accounting likely to include? A.Turnover costs B.Absenteeism C.Capitalization of salary D.Impact of positive employee attitude E.Gains from selection programs 52.Tim works at Ante Inc. He is responsible for conducting cost-benefit analyses of different functions performed at Ante. If Tim’s current task is to conduct a utility analysis, which of the […]
41. Which of the following statements is correct? A. If partners consider their cash withdrawals to be compensation for the work they do for the partnership, the amounts of the withdrawals should be charged to Salaries Expense. B. If there is no specific agreement on the division of partnership profits and losses, they are divided equally among the partners. C. If a salary is allowed to one […]
13.2 Learning Objective 13-2 1) Gross Accounts Receivable is $10,000. Allowance for Doubtful Accounts has a credit balance of $200. Net sales for the year are $150,000. In the past, 2% of sales had proved uncollectible. What would be the adjusted balance of the Allowance account under the income statement approach? A) $3,200 B) $2,800 C) $1,400 D) $3,000 2) Gross Accounts […]
136. All of the following statements related to current liabilities for U.S. GAAP and IFRS are true except: A. The definitions and characteristics of current liabilities are broadly similar for both U.S. GAAP and IFRS. B. The term provision is typically used under IRFS to refer to what is titled liability under U.S. GAAP. C. Because tax regulatory systems of countries are different, the approach to recording […]
81) Longview Golf Company sells a special putter for $20 each. In March it sold 28,000 putters while manufacturing 30,000. There was no beginning inventory on March 1. Production information for March was: Direct manufacturing labour per unit15 minutes Fixed selling and administrative costs $ 40,000 Fixed manufacturing overhead132,000 Direct materials cost per unit 2 Direct manufacturing labour per hour24 Variable manufacturing overhead […]
91. Accounting for just-in-time operations requires fewer transactions because A. large batches of inventory are combined in a smaller number of transactions.B. costs are accumulated in one department and then transferred to the next department.C. combined material and conversion costs are transferred to finished goods.D. costs are transferred from department to department allowing for better controls in costs. 92. Just-In-Time accounting has fewer transactions because A. all manufacturing costs are combined […]
202. 1) Bowen Company is completing its first year of operations on April 30, 2012. Reconstruct the entries for the year ended April 30, 2012 from the T accounts below. Record them as follows: A – L Journal Entries M- R Adjusting Journal Entries 2) Prepare the income statement, retained earnings statement, and classified balance sheet from the T accounts. […]
71.A company recorded office supplies in an asset account when the supplies were purchased. Failure to take inventory and make an adjusting entry will result in an a. overstatement of stockholders’ equity. b. understatement of liabilities. c. understatement of stockholders’ equity. d. understatement of assets. 72.A company’s five-day weekly payroll of $680 is paid on Fridays. Assume that the last day […]
Project 3 - Ensemble Methods and Unsupervised Learning
In this project you will explore some techniques in unsupervised learning
as well as ensemble methods. It is important to realize that understanding
an algorithm or technique requires understanding how it behaves under a
variety of circumstances. You will go through the process of choosing and
exploring two classification datasets, tuning the algorithms you have
learned about, writing a thorough analysis of your findings, and presenting
your findings. The most crucial part of this assignment is the analysis and
your ability to explain and justify your results.
I. Choosing Datasets
The first task in this assignment is choosing two interesting classification
datasets, these can be binary or multiclass. The features can be of any
type, and it is recommended that you choose datasets with diverse feature
sets. I don’t care where you get the data from. You can download some,
take some from your own research, or make some up on your own. What I
do care about is that the datasets must be interesting. They should
contain a decent amount of features and a sufficiently large amount of
examples. Do not choose an “easy” dataset, however don’t go crazy either
trying to find the perfect one. Your two datasets should also differ in some
way such that you can compare and contrast your results between the
two. You should also be following standard machine learning practice by
splitting your dataset into training and testing, and only touching the
testing dataset at the very end when you are ready to report results. (Cross
validation is highly recommended).
II. Coding (10%)
After choosing your datasets you will now be tasked with writing code to apply
the machine learning algorithms you have learned about. Your code must be
written in python, but you may use any libraries that have already implemented
the machine learning algorithms (e.g scikit-learn). You are not expected to code
the algorithms from scratch, and in fact I would highly discourage it. What you
may not do is copy code from the internet. Below are the analyses you are
required to run.
1) Run K-means and Hierarchical Clustering on your datasets and analyze
what you observe.
2) Run two dimensionality reduction algorithms (PCA and UMAP) on your
datasets. Observe and analyze the results.
3) Re-run the K-means and Hierarchical Clustering on your dimensionality
reduced datasets and compare the results to part (1).
4) Tune and train two ensemble models (AdaBoost and Random Forests) on
both your original and dimensionality reduced datasets. Compare and
analyze the results.
Your code does not have to be pretty or well written. However, it must be written
in python and I must be able to run one script (main.py) that will produce all the
results and figures in your report.
III. Report (80%)
You will then produce a report describing and analyzing your methods and
results. Here you will describe the datasets you have chosen and why they are
interesting. You will then provide an analysis on how the different machine
learning algorithms performed on each dataset. The report must be limited to 10
pages maximum. Plots and figures are highly recommended. It is up to you
how you wish to demonstrate your understanding of the machine learning
algorithms you have explored, but below I have listed some potential ideas for
analysis and items you may wish to include in the report.
• A description of your two datasets and why you feel that they are interesting.
• Hypotheses on how you believe the learning algorithms will perform on each
dataset and why.
• How you dealt with different features in your datasets? missing data? different
scalings?
• Training and testing error rates you obtained for your various learning
algorithms (some sort of cross validation is highly recommended)
• The effect of hyperparameters on performance
• Comparing and contrasting results between datasets
• Comparing and contrasting results between learning algorithms
• Training and testing error rates as a function of training dataset size
• Timing analysis of how long it takes to train/test each algorithm
• Conclusions
• Ideas for future analyses
• What you may have done differently
• References
You are NOT being graded on how well the algorithms perform on your datasets.
What is most important is WHY? You should be explaining and justifying all of
your figures and results, and demonstrating that you understand the intricate
details of the machine learning process, and the machine leaning algorithms you
are using.
IV. Presentation (10%)
Finally you will give a maximum 7 minute presentation of your results (You will be
cut off exactly at the 7 minute mark). In this presentation you will describe your
datasets, your methods, and any interesting results you found!
What to turn in?
Below is a list of items you will be required to turn in via canvas. Please make
sure all documents are named as described bellow.
• report.pdf - Your maximum 10 page report in pdf format. Do not use super
tiny or large font. No specific formatting is required but use common sense.
• presentation.pptx or presentation.key - Your presentation slides either in a
powerpoint or keynote document.
• code.zip - A zip file with all of the code you have written. Within the folder
there should a file called README.txt that contains instructions on how to run
your code, and a python file called main.py that will produce all figures and
plots in your report/presentation. I should be able to reproduce your results
easily.
• data.zip - A zip file that contains the two datasets you have chosen.
Grading
You are being scored on your analysis more than anything else. Roughly
speaking, implementing everything and getting it to run is worth very little for
this assignment. Of course though, analysis without proof of working code
makes the analysis suspect. The key thing is that your explanations should be
both thorough and concise, and your analysis should prove to me you have a
deep understanding of the machine learning process and the machine learning
algorithms you are using.
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