0-1. Intro

0. Intro > 0-1. Intro

Course Time

Lectures: Monday and Wednesday 2:30 – 3:45 pm , Room: Bruner 228

Course Schedule

Topics covered in the course:

Introduction

1. What, Why, and How?
2. Python

Part 1: Supervised Learning

1. Basic Algorithms: Linear regression, logistic regression, support vector machine (SVM), Neural Networks (NNs)
2. Anatomy of a learning algorithm
3. Basic practice: datasets, underfitting & overfitting, regularization, hyper-parameter optimization, performance assessment

Part 2: Unsupervised Learning

1. Clustering
2. Dimensionality reduction
3. Density estimation

Grading

CategoryPercentageDelivery Date

Assignments	15%
4 Quizzes	15%
Course Project	20%	April 24, 26th
Exam-1	25%	Tentative Date: Wednesday March 8th
Exam-2	25%	Monday May 1st, 2023, 1:00 – 3:00 pm

Remarks:

• Exam-2 covers the materials after Exam-1. In addition, chapter (review) quizzes are given where students are asked to solve exercises and answer questions from the whole chapter.
• Exams and Quizzes will b eopen book. Only lecture slides are allowed. The use of the Internet or other resources will result in a zero-exam grade.

References

No textbook (4 Reference)

1. The Hundred-Page Machine Learning Book, Andriy Burkov, Jan. 2019
2. Introduction to Data Mining, Tan, Steinbach, Karpatne, and Kumar, 2nd Edition, Pearson Education, 2019
3. Introduction to Machine Learning with Python: A Guide for Data Scientists, A. Muller and S. Guido, O’Reilly, 2016
4. Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, 2016

Course Details

Instructor: Dr. Muhammad Ismail, Office: Bruner 333, Email: mismail@tntech.edu
Prerequisite: Design of Algorithms, Fundamentals of Data Science

Course Project

Feb. 1st: A 1-page summary

April 24th: Final submission
Submission of the code and report in .ipynb format (on Google Colab) and presentation slides. The submission will include: Abstract: a summary of the report and main findings, Introduction: discussion of the problem in hand, any relevant solutions, datasets, limitations, Dataset: description and analysis (visualization) of the dataset used in the report, Methodology: description of the machine learning approaches adopted in the project, Results: performance evaluation results, comparisons, and discussions, Conclusion: main findings of the project.

April 24th & 26th: Presentation of the project (~ 8 minutes/team).

Late Policy

No late submission is accepted.