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python代写-EECS4404/5327-Assignment 2

时间：2021-02-07

York University EECS4404/5327 Intr to ML/PR (Winter 2021)

Assignment 2

Your Name (your student number)

abc@yorku.ca

February 4, 2021

Note: You have to work individually. You must use the same mathematical notations in text-

book or lecture slides to answer these questions. You must use this latex template to write up

your solutions. Remember to fill in your information (name, student number, email) at above.

No handwriting is accepted. In this assignment, you need to use the MNIST data set. Refer to

https://colab.research.google.com/drive/1FyahMGAE22716sUCrNXPvTrKTwS615Hd

for how to load it in Python. Direct your queries to Hui Jiang (hj@eecs.yorku.ca)

Exercise 1

Dimension Reduction

1. (5 marks) PCA: Q4.2 on page 93

2. (5 marks) LDA: Q4.4 on page 93

3. (10 marks) Data visualization: Lab Project I on page 92, parts a), b) and c)

Note that you will have to implement PCA and LDA from scratch but you may choose

to use a t-SNE implementation from any Python package.

Your answers:

Exercise 2

Linear Models for Regression

1. (10 marks) derive the formula to compute the gradients for the following linear models:

(a) linear regression

(b) ridge regression

(c) LASSO

follow the style of Algorithm 2.3 (refer to https://www.overleaf.com/learn/latex/

algorithms) to derive mini-batch stochastic gradient descent algorithms to optimize

these models.

2. (20 marks) implement these three algorithms on a small data set, e.g. the Boston Housing

Dataset (https://scikit-learn.org/stable/modules/generated/sklearn.datasets.

load_boston.html), to predict median value of a home from the 13 attributes. You need

to experimentally compare these regression models, discuss your results in terms of how

the learning objective function and all learned model weights may differ among three

models.

Your answers:

Department of Electrical Engineering and Computer Science 1

York University EECS4404/5327 Intr to ML/PR (Winter 2021)

Exercise 3

Support Vector Machine (SVM)

1. (10 marks) Q6.8 on page 130

2. (20 marks) use all training data of two digits 5 and 8 from the MNIST dataset to learn two

binary classifiers using linear SVM and nonlinear SVM (with Gaussian RBF kernel), and

compare and discuss the performance and efficiency of linear SVM and nonlinear SVM

methods for these two digits. Report your best results in the test data of 5 and 8. Don’t

call any off-the-shelf optimizer. Use the projected gradient descent in Algorithm 6.5 to

implement the SVM optimizer yourself.

Your answers:

What to submit?

You must submit:

1. one PDF document (using this latex template) for your solutions to all written questions

and all results and discussions for your programming assignments

2. one zip file that includes all of your Python codes and a readme file for TA to run your

codes

from eClass before the deadline. No late submission will be accepted.

Department of Electrical Engineering and Computer Science 2