Machine Learning

Information

Course Overview

1. An understanding of

   • The basic theory underlying machine learning.

   • A range of machine learning algorithms along with their strengths and weaknesses.

   • A very broad collection of machine learning algorithms and problems

2. Experience in

   • Appreciate the importance of visualization in the data analytics solution

   • Develop an appreciation for what is involved in learning from data. 

Course Objectives

• To Be able to:

  • Formulate machine learning problems corresponding to different applications.

  • Apply structured thinking to unstructured problems

  • Apply machine learning algorithms to solve problems of moderate complexity.

  • Apply the algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models. 

Reference Material

• Machine Learning: A Constraint-Based Approach 2nd edition, Morgan Kaufmann [view]

• Introduction to Machine Learning, Ethem Alpaydin, MIT Press-2016

Important Course Notes

Class Sessions

• Wednesday from 10:15am - 11:45am [Group AI-3] @Hall 1.

Office Hours

• Monday from 10:00am - 1:30pm

Grading Criteria

• Attendance - 5%

• Practical Assignment - 10%

• Midterm Exam - 25%

Late assignments and make-up

• Assignments submitted after the due date are docked 10% per day and will not be accepted for credit after a week. 

Lab & Workshops

• Lab Tools: Google Colab

Social Group

• Facebook group