EE 524 Machine Learning Laboratory

This is the course webpage for EE524. All announcements regarding tutorials, assignments, resources, deadlines will be posted here.

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Project maintained by snehilsanyal Hosted on GitHub Pages — Theme by mattgraham

Welcome to EE524 (Jul-Nov 2020)

Final submission for Projects: 6th December 2020

Viva Examination on 6th and 7th December 2020

Hello friends… Hope you are safe and sound.

I hope you are all excited to learn about Machine Learning. In this semester, you must be going through the Machine Learning Course offerred by Professor Manas Kamal Bhuyan sir.

This is the official course webpage for the course EE524 Machine Learning Laboratory which will complement the course Introduction to Machine Learning. We will be using Python an open source software to run our codes.

import os
import glob
import numpy as np
import pandas as pd
import tensorflow as tf
sess = tf.Session()
op = sess.graph.get_operations()
print([m.name for m in op]) #print the name of operations
print([m.values() for m in op]) #print the tensor produced by these operations

As mentioned, in the instruction manual we will also use libraries like Numpy, Matplotlib, Pandas and Scikit-Learn.

A breakthrough in Machine Learning

would be worth 10 microsofts.

-Bill Gates

A baby learns to crawl, walk and then run.

We are in the crawling stage, when it comes to applying Machine Learning.

-Dave Waters

Objectives of this lab:

1. Learn about the concepts in Machine Learning from data generation, handling, preprocessing.
2. Learn about different algorithms in Machine Learning.
3. Improve coding skills in Python.
4. Implement these concepts on your own.
5. Create something as an outcome of this lab course.

Lab evaluation Structure:

• Continuous Evaluations: 50 Marks (Through Assignments)
• Viva: 20 Marks (After all submissions)
• Mini Project: 30 Marks (Will be given after 6th Lab Session)

Timeline:

We have a total of 12 labs in this session. Everything will be online due to the pandemic. We will try to complete most of the important algorithms required for you to have a good background in Machine Learning. We will be pushing assignments, have doubt sessions and will evaluate your assignments. Assignments will be weekly.

• Weekly Assignment: Each Monday in the Outlook Group Grp_grp_ee-524ml-lab and Slack Channel
• Doubt Sessions: Every Friday via Microsoft Teams
• Assignment Submission: Every Sunday (EOD) in the assignments branch of this github repository.

Announcements:

• 7th December 2020 Viva for batch 2
• 6th December 2020 Final submission of projects and Viva for batch 1
• 23rd November 2020 Assignment 7 has been released, Project titles submission start
• 18th November 2020 Assignment 6 has been released
• 9th November 2020 Assignment 5 has been released
• 17th Oct 2020 Assignment 4 has been released
• 7th Oct 2020 Assignment 3 has been released
• 22nd Sept 2020 Assignment 2 has been released
• 7th Sept 2020: Assignment 1 has been released
• 11th Sept 2020: Doubt Session for Assignment 1 is over
• 17th Sept 2020: Assignment Submission link is released
• 20th Sept 2020: Submission Deadline for Assignment 1
• 22nd Sept 2020: Assignment 2 has been released
• 25th Sept 2020: Doubt session for submission and Assignment 2 is over
• 4th Oct 2020: Assignment 2 Submission Deadline.

Assignments:

1. Assignment 1 (Ungraded): Fundamentals of Python
   Release Date: 7th September 2020
   Doubt Session: 11th September 2020
   Submission Date: 20th September 2020
   Instruction Manual | PDF | Resources | Solution

2. Assignment 2 (Graded): Linear Regression and Error Surfaces
   Release Date: 22nd Sept 2020
   Doubt Session: 2nd Oct 2020
   Submission Date: 4th Oct 2020
   Assignment | Resources | Solution

3. Assignment 3(a) (Graded): Basic Plotting
   Release Date: 7th Oct 2020
   Doubt Session: 10th Oct 2020
   Submission Date: 11th Oct 2020
   Assignment | Resources | Solution

4. Assignment 4 (Graded): Bayes Theorem and Naive Bayes Classifier
   Release Date: 17th October 2020
   Doubt Session: 24th October 2020
   Submission Date: 27th October 2020
   Assignment | Resources | Solution

5. Assignment 5 (Graded): K Nearest Neighbours and K Means Algorithm
   Release Date: 9th November 2020
   Doubt Session: 13th November 2020
   Submission Date: 15th November 2020
   Assignment | Dataset | Solution

6. Assignment 6 (Graded): Support Vector Machines, PCA and LDA
   Release Date: 18th November 2020
   Submission Date: 29th November 2020
   Assignment | Dataset | Solution

7. Assignment 7 (Graded): Release Date: 23rd November 2020
   Submission Date: 29th November 2020
   Assignment | Dataset | Solution **

Team communication:

1. Slack Channel EE524 (For conversation and doubts)
2. Moodle Link (Theory course)
3. Outlook Group
4. Github Repository (For submitting assignments)

Resources:

1. Python Course
2. Youtube Machine Learning and Coursera Machine Learning
3. NPTEL PK Biswas Pattern Recognition
4. Numpy Tutorial
5. Pandas Tutorial
6. Matplotlib Tutorial
7. Youtube channels for your reference: Socratica| SentDex| Siraj Raval

Teaching Assistants:

Snehil Sanyal: Research Scholar, EEE Department, IIT Guwahati | Email: ssanyal@iitg.ac.in | Contact: 9399690211

Nadeem Atif: Research Scholar, EEE Department, IIT Guwahati | Email: atif176102103@iitg.ac.in | Contact: 7302099843

H Balaji: M.Tech 2nd year, EEE Department, IIT Guwahati | Email: hbalaji@iitg.ac.in | Contact: 9441791359