Home on Class Homepage

Categories #

Exceptional Circumstances #

Assignments (50%) #

There will be four assignments. Each one is designed to improve and test your understanding of the materials. Assignments will have both programming and written analysis components.

We have 4 big assignments in total. The reason we do not call them projects is because our class has a project as well. Consider each assignment as one individual big project. Assignments take time to finish them. YOU NEED TO START WORKING ON ASSIGNMENTS AS SOON AS THEY ARE OUT. Visit this course’s Canvas and GradeScope for the assignment documents. See the schedule table above for deliverable due dates. (Topics are subject to change):

Collection

Mon, 01 Jan 0001 00:00:00 +0000

GT Resources #

For any resources related to Student Engagement and Wellbeing such as Dean of Student’s contacts, Library, etc., please refer to this LINK

Class Resources #

Required Course Materials #

No textbook will be required for this course, however you are strongly encouraged to complete the readings indicated for each class. You may also find the following books very helpful:

Learning from data, by Yaser S. Abu-Mostafa
Pattern recognition and machine learning, by Christopher Bishop
Machine learning, by Tom Mitchell
Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber, and Jian Pei
The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Deep Learning, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Other resources, such as machine learning toolboxes and datasets, will be provided throughout the course.

Course Overview

Mon, 01 Jan 0001 00:00:00 +0000

Course Overview #

Important
CSE students should note that CS 7641 is not allowed as a substitute for the CSE core course CSE 6740, and that they cannot get credit for both CSE 6740 and CS 7641.

Course Description #

This course introduces techniques in machine learning with an emphasis on algorithms and their applications to real-world data. We will investigate the following question: how to computationally extract useful knowledge from data for decision making and task support! The course will also cover briefly Ethics in Machine Learning and Secure Computing. We will focus on machine learning methods, which are organized into three main course objectives:

General

Mon, 01 Jan 0001 00:00:00 +0000

General #

Attendance #

Our class will be offered on campus for both Undergrad (4641) and Grad (7641). Lectures might be recorded IF class has the recording system. Any class that I am able to record [which sometimes does not work even if we have the recording system in place], I will make it available to all students (both undergrad and grad) by the end of the day. The attendance is required for both undergrad and grad. Having students in the class helps me and my students A LOT work with each other for a better environment to facilitate learning. Trust me it will be fun and you will give me a lot of energy to teach better. The fact that you need to listen to the lectures without fast-forwarding me can help you learn the materials much better and you will have the chance to ask questions if you are confused anywhere in the lectures. Also, the class attendance will be counted toward your class participation at the end of semester.

Proposal

Mon, 01 Jan 0001 00:00:00 +0000

Proposal (5%) #

The primary goal of the proposal is to identify a problem that can be solved with machine learning. This includes finding and/or creating a dataset as well as developing a plan for the semester.

Note: As part of research, it is natural that the project may change from the original proposed. Please be sure to document and justify these changes in the midterm and final report.

Instructors and TAs

Mon, 01 Jan 0001 00:00:00 +0000

Instructors and TAs #

Instructors #

Max Mahdi Roozbahani

mahdir@gatech.edu

https://mahdi-roozbahani.github.io/

Head TAs #

Huili Huang

hhuang413@gatech.edu

TAs #

Richard Koulen

rkoulen3@gatech.edu

Ghazal Mirzazadeh

gmirzazadeh3@gatech.edu

Evan Cleveland

ecleveland6@gatech.edu

Jonathan Gil

jgil37@gatech.edu

Alexandra (Alex) Wu

xwu393@gatech.edu

Midterm Checkpoint

Mon, 01 Jan 0001 00:00:00 +0000

Midterm Checkpoint (10%) #

This is a checkpoint to make sure that you have had major progress in your project. By this point, at least one machine learning model should be implemented and evaluated. Results should be presented using visualizations and quantitative metrics. You will add information to your proposal to create your midterm report.

Note: As part of research, it is natural that the project may change from the original proposed. Please be sure to document and justify any such changes in this report.

Office Hours

Mon, 01 Jan 0001 00:00:00 +0000

Office Hours #

Overview #

Office hours will be in a hybrid mode for both online and in-person. We will send an announcement on Ed regarding office hours and when it will start. Please follow the instruction on the Excel Sheet provided on Ed discussion to signup for a slot with one of the TAs. You need to add your name and question of interest. If you require more minutes than the allocated one, please advise the TAs.

Print Website

Mon, 01 Jan 0001 00:00:00 +0000

Print Website #

Choose the pages you want, preview the combined document, and then use your browser’s print dialog to save it as a PDF.

Course Schedule (Mahdi)

Mon, 01 Jan 0001 00:00:00 +0000

Schedule #

Important
All deadline and due dates in this course will be at 23:59 EST.

Scroll horizontally to see the full schedule table on mobile devices

Week	Dates	Topics	Homework	Quizzes	Project	Readings
1	5/18 - 5/22	Course Overview (Notes, L1) Linear Algebra (Notes, L2)		Q0 - L1 \| Q1 - L2		GT Honor Code; Heilmeier catechism; Visual Information Theory by Chris Olah; GitHub Pages; YAML Configuration; Overleaf for GT students; Linear Algebra Review by Zico Kolter;
2	5/25 - 5/29	Memorial Day Holiday Prob and Stats (Notes, L3)	A1 out 5/25	Q2 - L3 \| Syllabus Quiz	Project team composition due 5/29	Independence, Coorelation, Orthogonality Correlation vs Covariance; The Differences Between Data, Information and Knowledge Cross Entropy as loss function More about Cross Entropy and KLD; Probability Theory Review by Andrew Moore;
3	6/1 - 6/5	Info Theory (Notes, L4) Optimization (Notes, L5) *Toolbox - P1/P2 (L6)		Q3 - L4, 5		Debugging Common Errors in NumPy; NumPy Tutorial; Matplotlib Tutorial; seaborn: statistical data visualization; KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial;
4	6/8 - 6/12	Clustering & K-Means (Notes L7) Hierarchical Clustering (Notes L7) GMM - Part 1 (Notes L8)	A1 due 6/12 A2 out 6/12	Q4 - L7		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN); Understanding the concept of Hierarchical clustering Technique; Dendrogram Visualization; GitHub Student Application
5	6/15 - 6/19	GMM - Part 2 (Notes L8) DBSCAN (Notes L9)		Q5 - L8 \| Q6 - L9	Project proposal due 6/19 Peer Evaluation 6/24	Jupyter Notbook (Kmeans and DBSCAN); KDE interactive visualization; KDE sampling; KDE SKLearn and sampling; Jupyter Notebook Kernel Density Example;
6	6/22 - 6/26	Dimension Reduction (Notes L11) Linear Regression (Notes L12)		Q7 - L11		Image reconstruction using PCA; Feature extraction using PCA; PCA for images; PCA as linear combination of features; PCA and Linear Discriminant Analysis;
7	6/29 - 7/3	Linear Regression (Notes L12) Regularization (Notes L13)	A2 due 7/3 A3 out 7/3	Q8 - L12		Simple Linear Regression in Matrix Format; Adding Noise to Regression Predictors;
8	7/6 - 7/10	Regularization - Contd (Notes L13) Project Practical Advice NB & Logistic Reg (*Notes L14)		Q9 - L13, 14	Project midpoint report 7/10 ( Note: You may submit it without penalty until 7/13. ) Peer Evaluation 7/17
9	7/13 - 7/17	Neural Networks (Notes L15) CNN (Notes L16)	A3 due 7/17 A4 out 7/17	Q10 - L15, 16		NN Playground Interactive NN initialization; The role of a hidden layer; Back propagation numerical example; More detailed introduction; CNN Live Demo; A guide to an efficient way to build CNN and optimize its hyper-parameters; Back Propagation in CNN; Transfer learning in CNN; Project Scoring Guidance;
10	7/20 - 7/24	DT (Notes, L17) RF (Notes, L17) SVM (Notes, L18)		Q11 - L17 \| Q12 - L18
11	7/27 - 7/28	SVM-Kernel (Notes, L19) Final Instructional Day	A4 due 7/27 can submit till 7/31 without any penalty		Final Project due 7/28 Peer Evaluation due 7/28	KKT and SVM Final Class days (July 28 and 29)

Final Report

Mon, 01 Jan 0001 00:00:00 +0000

Final Report (15%) #

The final report should present and compare at least 3 models that you have developed throughout the semester. Results should be presented using visualizations and quantitative metrics with a heavy focus on comparing the performance and tradeoffs of each approach. You will add information to your midterm to create your final report.

Note: As part of research, it is natural that the project may change from the original proposed. Please be sure to document and justify any such changes in this report.

Awards Galore

Mon, 01 Jan 0001 00:00:00 +0000

Why Awards Matter #

Awards serve as a tangible recognition of the dedication and creativity students bring to their machine learning projects. Each semester, these select honors are chosen from among hundreds of submitted projects, making the competition fierce and underscoring the ingenuity and motivation of participating teams. By highlighting outstanding work, we encourage students to push boundaries, refine technical skills, and communicate findings more effectively.

This recognition also benefits students’ academic and professional growth—award recipients can feature their projects on resumes and link directly to showcased work for added visibility. Additionally, all projects remain live on the class website for future semesters, ensuring that both current and prospective students can discover, learn from, and be inspired by past successes. This tradition fosters continuity in the course and builds a growing body of shared knowledge. Being featured on the website is thus both an honor and a motivator, inspiring future cohorts to strive for the same high standard of excellence.

Course Schedule (Nimisha)

Mon, 01 Jan 0001 00:00:00 +0000

Schedule #

Important
All deadline and due dates in this course will be at 23:59 EST.

Scroll horizontally to see the full schedule table on mobile devices

Week	Dates	Topics	Homework	Quizzes	Project	Readings
1	5/18 - 5/22	Course Overview (L1) Linear Algebra (Notes, L2)		Q0 - L1 \| Q1 - L2		GT Honor Code; Heilmeier catechism; Visual Information Theory by Chris Olah; GitHub Pages; YAML Configuration; Overleaf for GT students; Linear Algebra Review by Zico Kolter;
2	5/25 - 5/29	Prob and Stats (Notes, L3) Info Theory (Notes, L4)	A1 out 5/25	Q2 - L3 \| Q3 - L4 \| Syllabus Quiz	Project team composition due 5/29	Independence, Coorelation, Orthogonality Correlation vs Covariance; The Differences Between Data, Information and Knowledge Cross Entropy as loss function More about Cross Entropy and KLD; Probability Theory Review by Andrew Moore;
3	6/1 - 6/5	Memorial Day Holiday Optimization (L5) Toolbox - P1/P2 (L6)		Q4 - L5		Debugging Common Errors in NumPy; NumPy Tutorial; Matplotlib Tutorial; seaborn: statistical data visualization; KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial;
4	6/8 - 6/12	Clustering & K-Means (Notes L7) Hierarchical Clustering (Notes L7) GMM - Part 1 (Notes L8)	A1 due 6/12 A2 out 6/12	Q5 - L7 \| Q6 - L8		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN); Understanding the concept of Hierarchical clustering Technique; Dendrogram Visualization; GitHub Student Application
5	6/15 - 6/19	GMM - Part 2 (Notes L8) DBSCAN (Notes L9) Clustering Eval (Notes L10-A) Density Estimation (Notes L10-B)		Q7 - L9, L10-A (Density Estimaion NOT included)	Project proposal due 6/19 Peer Evaluation 6/24	Jupyter Notbook (Kmeans and DBSCAN); KDE interactive visualization; KDE sampling; KDE SKLearn and sampling; Jupyter Notebook Kernel Density Example;
6	6/22 - 6/26	Density Estimation - Contd (Notes L10-B) Dimension Reduction (Notes L11) Juneteenth Holiday		Q8 - L10-B (Density Estimaion included), L11		Image reconstruction using PCA; Feature extraction using PCA; PCA for images; PCA as linear combination of features; PCA and Linear Discriminant Analysis;
7	6/29 - 7/3	Linear Regression (Notes L12) Regularization (Notes L13)	A2 due 7/3 A3 out 7/3	Q9 - L12		Simple Linear Regression in Matrix Format; Adding Noise to Regression Predictors;
8	7/6 - 7/10	Regularization - Contd (Notes L13) Project Practical Advice NB & Logistic Reg (Notes L14)		Q10 - L13, 14	Project midpoint report 7/10 ( Note: You may submit it without penalty until 7/13. ) Peer Evaluation 7/17
9	7/13 - 7/17	Neural Networks (Notes L15) CNN (Notes L16)	A3 due 7/17 A4 out 7/17	Q11 - L15, 16		NN Playground Interactive NN initialization; The role of a hidden layer; Back propagation numerical example; More detailed introduction; CNN Live Demo; A guide to an efficient way to build CNN and optimize its hyper-parameters; Back Propagation in CNN; Transfer learning in CNN; Project Scoring Guidance;
10	7/20 - 7/24	DT (Notes, L17) RF (Notes, L17) SVM (Notes, L18)		Q12 - L17 \| Q13 - L18
11	7/27 - 7/28	SVM-Kernel (Notes, L19) Final Instructional Day	A4 due 7/27 can submit till 7/31 without any penalty		Final Project due 7/28 Peer Evaluation due 7/28	KKT and SVM Final Class days (July 28 and 29)

Course Schedule (Rod)

Mon, 01 Jan 0001 00:00:00 +0000

Schedule #

Important
All deadline and due dates in this course will be at 23:59 EST.

Scroll horizontally to see the full schedule table on mobile devices

Week	Dates	Topics	Homework	Quizzes	Project	Readings
1	5/18 - 5/22	Course Overview (L1) Linear Algebra (Notes, L2)		Q0 - L1 \| Q1 - L2		GT Honor Code; Heilmeier catechism; Visual Information Theory by Chris Olah; GitHub Pages; YAML Configuration; Overleaf for GT students; Linear Algebra Review by Zico Kolter;
2	5/25 - 5/29	Prob and Stats (Notes, L3) Info Theory (Notes, L4)	A1 out 5/25	Q2 - L3 \| Q3 - L4 \| Syllabus Quiz	Project team composition due 5/29	Independence, Coorelation, Orthogonality Correlation vs Covariance; The Differences Between Data, Information and Knowledge Cross Entropy as loss function More about Cross Entropy and KLD; Probability Theory Review by Andrew Moore;
3	6/1 - 6/5	Memorial Day Holiday Optimization (L5) Toolbox - P1/P2 (L6)		Q4 - L5		Debugging Common Errors in NumPy; NumPy Tutorial; Matplotlib Tutorial; seaborn: statistical data visualization; KKT for inequality constrained optimization; Why Cross Entropy over MSE for Classification; Gradient Descent short video; Matplotlib Tutorial; NumPy Tutorial;
4	6/8 - 6/12	Clustering & K-Means (Notes L7) Hierarchical Clustering (Notes L7) GMM - Part 1 (Notes L8)	A1 due 6/12 A2 out 6/12	Q5 - L7 \| Q6 - L8		Curse of dimensionality (Euclidean space example); Jupyter Notbook (Kmeans and DBSCAN); Understanding the concept of Hierarchical clustering Technique; Dendrogram Visualization; GitHub Student Application
5	6/15 - 6/19	GMM - Part 2 (Notes L8) DBSCAN (Notes L9) Clustering Eval (Notes L10-A) Density Estimation (Notes L10-B)		Q7 - L9, L10-A (Density Estimaion NOT included)	Project proposal due 6/19 Peer Evaluation 6/24	Jupyter Notbook (Kmeans and DBSCAN); KDE interactive visualization; KDE sampling; KDE SKLearn and sampling; Jupyter Notebook Kernel Density Example;
6	6/22 - 6/26	Density Estimation - Contd (Notes L10-B) Dimension Reduction (Notes L11) Juneteenth Holiday		Q8 - L10-B (Density Estimaion included), L11		Image reconstruction using PCA; Feature extraction using PCA; PCA for images; PCA as linear combination of features; PCA and Linear Discriminant Analysis;
7	6/29 - 7/3	Linear Regression (Notes L12) Regularization (Notes L13)	A2 due 7/3 A3 out 7/3	Q9 - L12		Simple Linear Regression in Matrix Format; Adding Noise to Regression Predictors;
8	7/6 - 7/10	Regularization - Contd (Notes L13) Project Practical Advice NB & Logistic Reg (Notes L14)		Q10 - L13, 14	Project midpoint report 7/10 ( Note: You may submit it without penalty until 7/13. ) Peer Evaluation 7/17
9	7/13 - 7/17	Neural Networks (Notes L15) CNN (Notes L16)	A3 due 7/17 A4 out 7/17	Q11 - L15, 16		NN Playground Interactive NN initialization; The role of a hidden layer; Back propagation numerical example; More detailed introduction; CNN Live Demo; A guide to an efficient way to build CNN and optimize its hyper-parameters; Back Propagation in CNN; Transfer learning in CNN; Project Scoring Guidance;
10	7/20 - 7/24	DT (Notes, L17) RF (Notes, L17) SVM (Notes, L18)		Q12 - L17 \| Q13 - L18
11	7/27 - 7/28	SVM-Kernel (Notes, L19) Final Instructional Day	A4 due 7/27 can submit till 7/31 without any penalty		Final Project due 7/28 Peer Evaluation due 7/28	KKT and SVM Final Class days (July 28 and 29)