OIDD 255X Quiz Guidelines
This document gives you some guidelines for your in-class exam on Wednesday Feb 16th. It is aimed
at helping you prepare efficiently for the exam, and gives you some detailed pointers. Remember, this
is a study guide, and not an algorithm. Beyond these guidelines, use your best judgment.
Date, time, duration and location of the exam
Date and time: Wednesday Feb 16th (normal class time, normal class room).
Duration: The quiz is intended to be fairly short. You will be allowed no more than 45 minutes to
complete the exam. You may leave when finished.
Format of the exam
The exam is closed book/notes/device (just you, pens or pencils, a snack or drink, and the exam
booklet). You do not need a calculator. It is entirely on paper, and there is no electronic or online
component. The exam may include some or all of the following types of questions: True/False, multiplechoice, matching, fill-in-the-blanks, analysis, short-answer (2-3 sentences). Provided you have followed
what we have done in class and done the required reading, there will be no “trick” questions, or unfairly
difficult or obscure questions. You will not be tested on your general knowledge of business — if we
base any questions on businesses that we have not discussed in class or that appeared in the
assigned readings, we will provide you with the details you need.
Sessions covered on the exam
The exam will cover the sessions shown below. Please note that the first two sessions that took place
on Zoom (Intro to Course and a History of AI) will NOT be covered.
Starting from Session 3, anything we have discussed in class, that appears on a slide, that is part of the
required readings, or that you have been seen in the Labs is fair game, so read through your notes,
slides, and assignments. Specifically, information from the following session topics could be covered on
the exam:
The AI Stack: Data and Digitization
The AI Stack: Networks
How AI Works, Part 1
How AI Works, Part 2
How Deep Learning Works
Lab 1: Diabetes Prediction
Lab 2: HR Attrition
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Readings and videos for which you are responsible
AI and Moore's Law
Net Neutrality Primer
John Oliver’s Commentary on Net Neutrality
A Visual Introduction to Machine Learning
The Complete Beginner’s Guide to Machine Learning (up until the section entitled “Quantitative vs.
Categorical Data”)
About Train, Validation and Test Sets in Machine Learning
Performance metrics for classification models
No coding required: Companies make it easier than ever
Want to know how deep learning works?
When and When Not to Use Deep Learning
Use cases for deep learning across industries
Things we will not ask you
You will not be tested on material that appears in any OPTIONAL readings or videos.
You do not need to remember the sequence of steps that you take in Excel or WEKA to perform
any operations. More broadly, we will not ask you questions about the use of other specific
software.
We will not ask you to expand acronyms, or to reproduce obscure facts from the readings or slides.
Suggestions for preparing for the exam
Using your class notes and any slides that might have been posted on Canvas, make sure you have a
thorough grasp of the learning objectives in the beginning of the slides for most of the sessions. Rather
than memorizing, try to improve your understanding of what aspects of technology are important, and
why. Next, read through the required readings that were not explicitly covered in class. Think, for
example, about why Moore’s Law is important for AI progress. Make sure you understand how ML
works and how it is different from automated software rules. Make sure you understand how deep
learning differs from traditional “shallow” machine learning techniques.
You are expected to know how information like text and music is converted to bits and Mega bits. You
will be expected to know how to compute error metrics like Precision and Accuracy as we did in class.
For concepts like Moore’s Law, we could ask you to describe the concept, or we could ask you to do
some analysis that tests your understanding of these concepts. We may also ask you conceptual
questions about what Net Neutrality implies, or we may ask you to define basic terms related to
machine learning.
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Vocabulary with which we expect you to be familiar
Bits, Bytes, Transistors, Integrated Circuits, Moore’s Law, Quantum Computing, Qubits
TCP/IP, Internet Backbone, Internet Service Provider (ISP)
Packet-switched network, Circuit-switched network
IP Address, Routers, Net Neutrality, Cloud Computing
Domain Expertise, Expert systems
Accuracy, Precision, Sensitivity, Recall, Specificity
True Positive, True Negative, False Positive, False Negative, Type I errors, Type II errors
Confusion Matrix, ROC Curve, Training data, Test data, Validation data, Overfitting, Pruning
Deep learning, Structured data, Unstructured data, Feature engineering/extraction
Neural networks, Activation functions
Loss function, Back Propagation, Gradient descent
Batch size, Epochs, Learning Rate, Regularization, Hidden layers
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