Test Information
Updated March 29, 2022 • Information about tests for the course.
Final Exam Logistics
Date and Time:
 The Final Exam will be held during the official exam on Friday April 8 at 7:30pm in M3 1006.
 It will be scheduled and run by the campus exam operations, so there will be strict protocols for entry and exit as well as notes and materials.
 Make sure you know where the room is before April 8!
Final Exam Scope
Scope: Content from live lectures and selfstudy videos from the entire course, but focussed on the new concepts learned in *weeks 9  12
Topics: High level knowledge about topics from the entire course, including all Deep Learning topics, see detailed breakdown below.
Final Exam”CheatSheet” and Exam Materials
As for the midterm, you can bring three sheets of paper (US Letter or A4), double sided, with hand written notes on it of any kind. I say handwritten because when I’ve allowed it to be printed in the past, many people end up just getting a shared document from their peers, and the best thing about a “cheat sheet” is how much you remember and learn by making it yourself. Studies show that you learn much better from notes written by hand under space or time constraints than electronic reference documents. You also bring cheatsheet again, so a total of six pages.
 Pen and pencil (a mechanical pencil is suggested for multiple choice questions so you can erase it if needed).
 An eraser :)
 Calculator  for arithmetic calculations
 Your midterm cheatsheet, up to three pages of notes for first half of course
 You final exam cheatsheet, up to three additional pages of notes
 Valid UWaterloo student ID (WatCard)
Final Exam Question Topics
Topics from before the midterm
Questions may require high level knowledge about topics from the entire course. This is most likely for topics which relate to training a Deep Learning model (eg. Crossvalidation, Train/Validate/Test, Error measures, Distance metrics,…)
For all topics you should:
 Know definitions of methods and algorithms.
 Know relative strengths and weaknesses between them, when they apply.
 Perform small calculations of some core algorithms, formulas and metrics.
 Visually represent the expected behaviour of some algorithms on presented data.
 Be familiar with the overall flow of Data Analysis from data preprocessing, feature analysis, algorithm choice and application, experimental design and validation.
Neural Network Fundamentals
 Relation to logistic regression
 Network Design Considerations (width, depth)
 Input, hidden, output units
 Activation functions
 Comparison to other supervised learning algorithms
 Types of Deep Learning: Fully Connected, CNN, RNN, Autoencoders
Effective Deep Learning Training Methods:
 Problems with NNs  Vanishing Gradients Problem, Catastrophic Forgetting, Overfitting, etc.
 Regularization methods  L1/L2 penalties, dropout, early stopping, data augmentation
 Model capacity learning curves
 Backpropagation algorithm
 Understanding core concepts about gradient descent
 Benefits of various Gradient Optimizers
 Practical Methodology for building Deep Learning systems
For CNNs

Benefits, pros/cons, general design

Analysis of impact of a given filter

Identify difference between convolution, deep, sparsely connected structures

Pooling, downsampling, strides, padding
For Autoencoders
 standard autoencoders
 denoising autoencoders
For the Following Complex Methods
The following architectures or methods were introduced and talked about at a high level. So questions could relate to them, but would be limited to
 understanding purpose and usage of these architecture/methods
 questions would not require detailed implementation or analysis
List of Complex Methods:
 Inception, Resnet, Densenet
 LSTM, LRCN
 Recurrent Neural Networks
 Variational Autoencoders
 ~BERT~
Final Exam Question Types:
 True or False [1 mark each] these questions merely have a true or false answer to choose
 Multiple Choice [24 marks each]  all questions have one correct answer only, some will include “all of the above” as an option. If “all of the above” is the correct answer, then check one other answer will receive a zero for that question.
 Data/Output/Algorithm Matching [24 marks each]
 Given a visualization of a dataset and the resulting output of an algorithm: identify the algorithm that was used
 Distinguish the parameters of that algorithm from a set of choices
 MCx2 [4 marks each]  Some multiple choice questions have a followup, such as how to improve the situation from the first part.
 CNN Calculation Questions (see below)
 DNN Architecture Questions (see below)
 Other Potential Topics for Calculations and Formula questions:
 Write down, or identify from a list, the correct mathematical form for a given Neural Network in terms of:
 the full neural network discriminant function for a given design or
 the back propagation update or
 the gradient for some component of the network or
 the loss function
 Write down, or identify from a list, the correct mathematical form for a given Neural Network in terms of:
CNN Calculation Questions [5 points each]
Actual setup text for questions:
“Consider the following convolutional neural network and answer the questions below: The network takes input images of size (?x?) with ? colour channels. The first convolutional layer uses filters of size ?, a stride of ?, and zeropadding of width ?.”
 What will be the dimensions of the resulting activation maps of this first layer?”
 Choose from options in the form (? x ? x ?)
 “Consider the following convolutional neural network and answer the questions below:
 The 4x4 layer is provided as input to a pooling layer as described in each question, which output would have the correct values in each situation?”
 You are given a 4x4 matrix of integers as the input to the pooling calculation
 “Which of the following outputs would be correct if we were using max pooling, with a ?x? pooling filter and a stride of ? ?”
 “Which of the following outputs would be correct if we were using average pooling, with a ?x? pooling filter and a stride of ? ?”
 Choices are presented as 2x2 matricies of numbers to choose from.
DNN Architecture Question
“Which of the following formulations is the correct one for the output of the following Neural Network architecture:
 This network takes a flat vector of numeric input \(X\) , using three fully connected hidden layers where:
 The first hidden layer has ? nodes, the second hidden layer has ? nodes, and the third hidden layer has ? nodes.
 The first and second hidden layers use ? as the activation function. “
Definitions:
 \(x_i\) : input data values
 \(f_A(x)\) : activation function for type \(A\) where:
 \(A\in\{R=RELU, M=Softmax, S=Sigmoid, E=ELU, G=Gaussian\}\).
 \(w^{(c)}_{ab}\) : hidden weights for layer \(c\), connecting incoming feature \(b\) from the previous layer to the hidden unit \(a\).
 \(a*b\) : normal, pairwise multiplication of number \(a\) and \(b\), used for reading clarity.
Which net activation output if the correct one? (multiple formulae or the following form will be presented to choose from)
 \(net_m = f_E\left( \sum_{k=1}^{5} w^{(3)}_{mk} * f_E\left( \sum_{j=1}^{10} w^{(2)}_{kj} * f_E\left( \sum_{i=1}^{15} w^{(1)}_{ji} * x_i + w^{(1)}_{j0} \right) + w^{(2)}_{k0} \right) + w^{(3)}_{m0} \right)\)
Midterm Exam
Some more information on the midterm will be up soon on LEARN with sample questions, details about format and details about notes.
The midterm will be held, as planned, on March 1, during class time and inperson.
 If you have accessibility concerns, there is an official procedure for that through AccessAbiltiy Services (https://uwaterloo.ca/accessabilityservices/).
 If you cannot attend the midterm inperson, you need to talk to Prof. Crowley about it to explain and make other arrangements.
 Note that the Final Exam will be inperson, and you are expected to attend it in person. Existing university policies around missed exams will apply.
 The Final Exam will be held during the official exam on Friday April 8 at 7:30pm in M3 1006.
 It will be scheduled and run by the campus exam operations, so there will be strict protocols for entry and exit as well as notes and materials.
 Make sure you know where the room is before April 8!
Midterm Topics
At a high level, the topics of midterm will cover any content from Course Weeks 1  6 from Data Understanding and Preprocessing up to Decision Trees and Ensemble Methods.
Midterm “CheatSheet”
For the midterm you can bring one sheet a paper (US Letter or A4), double sided, with hand written notes on it of any kind. I say handwritten because when I’ve allowed it to be printed in the past, many people end up just getting a shared document from their peers, and the best thing about a “cheat sheet” is how much you remember and learn by making it yourself. Studies show that you learn much better from notes written by hand under space or time constraints than electronic reference documents. When the Final Exam comes, you will be allowed to bring your midterm cheatsheet and at least one additional page of notes.