Description
1 20 Newsgroups predictions (6%)
In this question you will work with the 20 newsgroups dataset. This dataset contains posts from 20
different newsgroups and the task is to classify each post to the correct newsgroup. Your goal will
be to evaluate several models of your own choice and select those with the best test performance
on this task.
We provide code that takes the original text and converts it into a bag-of-words counts representation. We also provide code that converts it to tf − idf features, these features reweigh words
according to frequency (so a common uninformative word like ”the” would get a small weight)
you can read about these features here https://www.tfidf.com/. You can work with either feature representation.
We also provide starter code to train and evaluate a very simple Bernoulli Naive-Bayes classifier as
the baseline model. You should implement better models for this task and outperform the baseline.
• You may use any open-source code you want, as long as the algorithm was covered in class.
• You need to report the results of three different algorithms (you are encouraged to try out
more and pick 3 out of them) and the baseline.
• Each result reported should include train and test loss.
• You are to report evaluations on the test loss exactly 4 times. Once for each of your proposed
model and a final time for the baseline.
• Explain in your report how you picked the best hyperparameters.
• Explain why you picked these 3 methods, did they work as you thought? Why/Why not?
• For your best classifier compute and show the confusion matrix, which is a k×k matrix where
Cij is the number of test examples belonging to class j that were classified as i. This part you
need to write yourselves.
• What were the two classes your classifier was most confused about?
2 Training SVM with SGD (5%)
In this question you will implement Stochastic Gradient Descent with momentum β and learning
rate α. You will then use your implemented algorithm to approximately optimize the SVM objective.
2.1 SGD With Momentum
1. Implement SGD with momentum.
2. To verify your implementation find the minimum of f(w) = 0.01w
2 using gradient descent.
Take w0 = 10.0 and set your learning rate to α = 1.0. Plot wt for 200 time-steps using β = 0.0
and β = 0.9 on the same graph.
2.2 Training SVM
We denote the parameters of the SVM model by w and consider a dataset made up of N pairs
(x
(i)
, y(i)
) where x are continuous features and y ∈ {±1} are the binary class labels.
The goal is to find
w∗
, b∗ = arg min
1
2
||w||2 +
C
N
X
N
i=1
max(1 − y
(i)
(wT x
(i) + b), 0)
Write a function that takes as input:
• Data x
(1)
, . . . , x
(N) ∈ R
d and y
(1), . . . , y(N) ∈ {±1}.
• Penalty parameter C of the error term
• Mini-batch size m, constant learning rate α and number of iterations T.
The function will perform T steps of SGD with batch size of m, learning rate α, and momentum β.
The output will be the final weights wT .
A few notes:
• For gradient computation, you can take the gradient at the ”kink” y
(i)wT x
(i) = 1 to be zero.
• You may include the bias implicitly by adding a column of ones to your data matrix. But
remember not to regularize the bias weight!
• You should sample the mini-batches without replacement.
• Finally note that momentum may not have a significant effect on SVM training.
2.3 Apply on 4-vs-9 digits on MNIST
MNIST is a digit classification dataset consisting of 28 × 28 grayscale images of hand-drawn digits
labelled from 0 to 9.
We turn it into a binary classification problem by trying to classify 4 vs 9 (the hardest 1-vs-1 pair)
and discarding the rest of the classes. The remaining images are separated into 80% train and 20%
test. We convert the images to vectors by flattening them to a vector of size 784.
Train two SVM models using gradient descent with a learning rate of α = 0.05, a penalty of C = 1.0,
minibatch sizes of m = 100, and T = 500 total iterations. For the first model use β = 0 and for the
second use β = 0.1. For both of the trained models report the following:
1. The training loss.
2. The test loss.
3. The classification accuracy on the training set.
4. The classification accuracy on the test set.
5. Plot w as a 28 × 28 image.
We chose these hyperparameters as they seemed to work well (∼ 90% accuracy) – you might want to try cross
validation to see how big of an improvement you can get!
3 Kernels (4%)
In this question you will prove some properties of kernel functions. The two main ways to show a
function k(x, y) is a kernel function is to find an embedding φ(x) such that k(x, y) = hφ(x), φ(y)i
or to show the for all x
(1)
, . . . , x
(n)
the gram matrix Kij = k(x
(i)
, x
(j)
) is positive semi-definite (i.e.
symmetric and no negative eigenvalues).
3.1 Positive semidefinite and quadratic form
1. Prove that a symmetric matrix K ∈ R
d×d
is positive semidefinite iff for all vectors x ∈ R
d we
have x
T Kx ≥ 0.
3.2 Kernel properties
Prove the following properties:
1. The function k(x, y) = α is a kernel for α > 0.
2. k(x, y) = f(x) · f(y) is a kernel for all f : R
d → R.
3. If k1(x, y) and k2(x, y) are kernels then k(x, y) = a·k1(x, y)+b·k2(x, y) for a, b > 0 is a kernel.
4. If k1(x, y)is a kernel then k(x, y) = √
k1(x,y)
k1(x,x)
√
k1(y,y)
is a kernel (hint: use the features φ such
that k1(x, y) = hφ(x), φ(y)i).
Submission
Assignments must be submitted by 10:00 pm on December 3rd; a late penalty of 10% per day will
be assessed thereafter (up to 3 days, then submission is blocked). We highly recommend using
Python 3.6 embedded in Anaconda 3.4 to solve the programming questions. However, you are welcome to use any programming language you like, given that your code solves the problem, and is
functional and modular.
What to submit
• All work to be submitted via Markus.
• All mathematical work has to be clearly marked by the question it belongs to, we highly
recommend using a math editor such as MathType, Word Equations or LaTex. However, if
you prefer to use paper and pen(cil), you are welcome to do so as long as your hand writing
is readable. Unreadable work will result in losing the full mark of the question.
• Your written report should be submitted in PDF format. Note that Markus has filesize limits
and it is your responsibility to ensure that your work is uploaded on time.
• Your code must be clearly structured with an obvious entry point.
• All graphs and plots have to be clearly marked by their question. Use readable grids whenever
applicable.
