CS 4641\7641 A: Machine Learning (Spring 2022)

Course Information

  • Undergrad 4641 Lecture: Tuesdays and Thursdays, 9:30am - 10:45am EST
  • Undergrad 4641 Location: College of Business 100
  • Grad 7641 Lecture: Tuesdays and Thursdays, 12:30pm - 1:45pm EST
  • Grad 7641 Location: Howey Physics L1
  • Edstem: https://edstem.org/us/courses/16925
Mahdi Roozbahani
Head TA:
Ruijia Wang
Head TA:
Ruslina Utomo (Rusty)
Gururaj Deshpande
Sidharta Vadaparty (Sid)
Rohit Das
Kevin Li
Abdullah Shah
Jacob (Jake) Present
Mengyang Liu
Sidhesh Desai
Xuhui Zhou
Sarah Dongyun Bi
Khalil Sayid
Sivagami Subramanian Nambi
Meghana Deepak
Bryan Zhao
Dimitri Adhikary
Daanish M Mohammed
Patrick Crawford

Course overview

  1. Basic math for data science and machine learning

    • Linear algebra
    • Probability and statistics
    • Information theory
    • Optimization
  2. Unsupervised machine learning for data exploration

    • Clustering analysis
    • Dimensionality reduction
    • Kernel density estimation
  3. Supervised learning for predictive data analysis

    • Tree-based models
    • Support vector machines
    • Linear classification and regression
    • Neural networks

Prerequisites for this course include (1) basic knowledge of probability, statistics, and linear algebra; (2) Basic programming experience in Python.

In addition to the technical content, this class includes the following learning objectives:

  • Structuring a task into a machine learning work flow
  • Collaborating effectively on team projects in a remote environment
  • Conducting peer evaluation in a constructive format
  • Communicating technical content in a concise and effective manner


Date Class Assignments Project Quizzes Readings
Tue, Jan 11 *Course overview;
Refer to Lecture1 video on Canvas
Edstem signup GT Honor Code
Wed, Jan 12
Thu Jan 13 *Data analysis toolbox;
*Broadcasting toolbox;
Refer to Lecture2 video on Canvas
Heilmeier catechism;
Visual Information Theory by Chris Olah;
GitHub Pages;
YAML Configuration;
NumPy Tutorial;
Matplotlib Tutorial;
seaborn: statistical data visualization;
Overleaf for GT students;
Mon, Jan 17 MLK Holiday  
Tue, Jan 18 *Foundations: Linear algebra
*Class notes;

Refer to Lecture3 video on Canvas
Correlation vs Covariance
Linear Algebra Review by Zico Kolter
Wed, Jan 19 Q0: Introduction, pre-test
Thu, Jan 20 *Foundations: Probability and statistics;
*Class Notes;

Refer to Lecture4 video on Canvas
A1 out Probability Theory Review by Andrew Moore;
Mon, Jan 24   Q1: Linear algebra, and probability
Tue, Jan 25 *Foundations: Information Theory;
*Class Notes;
Refer to Lecture5 video on Canvas
The Differences Between Data, Information and Knowledge;
More about Cross Entropy and KLD;
Cross Entropy as loss function;
Wed, Jan 26 Project QA and Seminar by Guru and Ruijia  
Thu, Jan 27 *Foundations: Optimization;
Refer to Lecture6 video on Canvas
KKT for inequality constrained optimization;
Why Cross Entropy over MSE for Classification;
Gradient Descent short video
Mon, Jan 31 Project Seminar 2 by Bryan and Patrick Q2: Information Theory and Optimization
Tue, Feb 1 *Data analysis toolbox - part 2;
*Class Notes
Refer to Lecture7 video on Canvas
Project team composition due NumPy Tutorial;
Matplotlib Tutorial;
Wed, Feb 2
Thu, Feb 3 *Clustering Analysis and K-Means;
*Class Notes;
Refer to Lecture8 video on Canvas
Curse of dimensionality (Euclidean space example);
Jupyter Notbook (Kmeans and DBSCAN);
Mon, Feb 7 Q3: Data Analysis toolbox and Clustering\KMeans
Tue, Feb 8 *Gaussian Mixture Model;
*Class Notes;
Refer to Lecture9-part1 video on Canvas
GitHub Student Application;
Wed, Feb 9
Thu, Feb 10 *Gaussian Mixture Model Class Notes;

Refer to Lecture9-part2 video on Canvas
A1 due | A2 out
Mon, Feb 14 Project Forums Q4: GMM
Tue, Feb 15 *Hierarchical Clustering;
*Class Notes;
Refer to Lecture10 video on Canvas
Understanding the concept of Hierarchical clustering Technique;
Dendrogram Visualization ;
Wed, Feb 16
Thu, Feb 17 *Density-Based Clustering;
*Class Notes;
Refer to Lecture11 video on Canvas
Jupyter Notbook (Kmeans and DBSCAN);
Mon, Feb 21 Project Seminar 3 by Sarah Q5: Hierarchical and DBSCAN
Tue, Feb 22 *Evaluation of Clustering Algorithms;
*Class Notes;
Refer to Lecture12 video on Canvas
Wed, Feb 23
Thu, Feb 24 *Density Estimation
*Class Notes;

Refer to Lecture13 video on Canvas
Project proposal due
KDE interactive visualization ;
KDE sampling ;
KDE SKLearn and sampling ;
Jupyter Notebook Kernel Density Example;
Mon, Feb 28   First Peer Feedback Q6: Cluster evaluation and Density Estimation
Tue, Mar 1 *Dimensionality reduction;
*Class Notes;
Refer to Lecture14 video on Canvas
Image reconstruction using PCA ;
Feature extraction using PCA ;
PCA for images ;
PCA as linear combination of features ;
PCA and Linear Discriminant Analysis ;
Wed, Mar 2  
Thu, Mar 3 *Linear Regression;
*Class Notes;
Refer to Lecture15 video on Canvas
A2 due | A3 out Simple Linear Regression in Matrix Format;
Adding Noise to Regression Predictors;
Mon, Mar 7 Project Seminar 4 by Khalil and Daanish Q7: Dimensionality reduction
Tue, Mar 8 *Linear Regression Clas Notes [Contd];
*Regularization and Linear Regression;
*Class Notes;
Refer to Lecture16 video on Canvas
Wed, Mar 9
Thu, Mar 10 *Regularization and Linear Regression Class Notes[Contd];
Refer to Lecture17 video on Canvas
Mon, Mar 14 Q8: Linear Regression (Lecture 15 and 16)
(Daylight saving time)
Tue, Mar 15 *Naïve Bayes and Logistic Regression;
*Class Notes;
Refer to Lecture18 video on Canvas
Wed, Mar 16
Thu, Mar 17 *Naïve Bayes and Logistic Regression Class Notes;
Refer to Lecture19 video on Canvas


Q9: Regularization (Lecture 17)

Due on Mar 19th AOE

Mon, Mar 21  
Tue, Mar 22 No class - Spring break
Wed, Mar 23
Thu, Mar 24 No class - Spring break
Mon, Mar 28 Q10: Naive Bayes and Logistic Regression
Tue, Mar 29 *Neural Networks(Forward pass and Back propagation);
*Class Notes;
Refer to Lecture20 video on Canvas
  NN Playground ;
Interactive NN initialization ;
The role of a hidden layer;
Back propagation numerical example;
More detailed introduction;
Wed, Mar 30
Thu, Mar 31 *Neural Networks Class Notes;
*Neural Networks and Deep learning (CNN);
*Class Notes;
Refer to Lecture21 video on Canvas
A3 due | A4 out 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;
Fri, Apr 1 Project Seminar 5 by Rohit and Sidharta
Mon, Apr 4 Q11: NN
Tue, Apr 5 *Neural Networks and Deep learning Class Notes(CNN);
*Decision Tree;
*Decision Tree Class Notes;
Refer to Lecture22 video on Canvas

Project midpoint report

Wed, Apr 6 Second Peer Feedback
Thu, Apr 7 Decision Tree Class Notes;
*Ensemble Learning and Random Forest;
*Ensemble Learning Class Notes;
Refer to Lecture23 video on Canvas
Mon, Apr 11 Q12: CNN, Decision Tree, Ensemble Learning
Tue, Apr 12 *Support Vector Machine;
*Class Notes;

Refer to Lecture24 video on Canvas
KKT and SVM;
Wed, Apr 13  
Thu, Apr 14 *Support Vector Machine Class Notes;
*Kernel Method \ SVM;

*Kernel Method \ SVM Class Notes;
Refer to Lecture25 video on Canvas
Mon, Apr 18 Project Seminar 6 by Abdullah and Meghana Q13: SVM
Tue, Apr 19 *Kernel Method \ SVM Class Notes
Refer to Lecture26 video on Canvas
Wed, Apr 20
Thu, Apr 21 *Ethics in ML;
Refer to Lecture27 vsideo on Canvas
A4 due

Q14: Kernel Method

Due on Apr 23rd AOE

Mon, Apr 25   Final Instructional Class Day
Tue, Apr 26 *ML Course review; -Final project due
-Third Peer Feedback
Final Instructional Class Day

Course policies

  • 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 to 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 to 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.
  • Class deliverables: All class deliverables will be handled via Gradescope except quizzes which will be on Canvas. The time span offered to complete the course objectives is plentiful and deadlines will not be extended under any circumstances. To ensure the class is fair for all students, you will receive zero credit for work submitted after the deadline. Regrade requests should be submitted directly on Gradescope within a defined period after grade publication (we will inform you on that; we usually give one week for the regrade request; but it may change depending on our class schedule). Should you find yourself in an impasse with the TA responsible for your grading, feel free to contact the head TA or course instructor on Edstem.
  • Edstem:
    • Edstem will be the main and only place for the course discussions and announcements. If you have questions, please ask it on Edstem first because 1) other students may have the same question; 2) you will get help much faster.
    • If it’s something you do not like to discuss publicly on Edstem, you can use private messaging on Edstem.
    • Anytime you want to send a private message to just me on Edstem, please make sure to add our HEAD TAs too in case I may miss your message.
    • Edstem GOOD questions
      • I don't understand this part of the lecture, can you explain it to me?
      • This certain part of the hw is not clear to me, would it be possible to explain that more?
      • I have a question about the project ...
      • I found an issue on the website, hw or the lectures, can you clarify ...
      • Any feedback, suggestions, ... would be greatly appreciated.
      • Historically, most of the questions were good.
    • Edstem BAD questions
      • Can you debug my code? [our team will not do that. You need to be specific about your question]
      • Can you find where the problem is in my code?
  • Exceptional circumstances: Any request for exceptions to these policies should be made in advance when at all possible. Requests should be due to incapacitating illness, personal emergencies, or similarly serious events. Your request MUST be accompanied by a supporting letter issued by the Dean of Students before contacting us.

Diversity and inclusion

Just as machine learning algorithms cannot accomplish complex tasks if trained on datasets of limited variability, our course cannot be successful without appreciating the diversity of our students. In this class we aim to create an environment where all voices are valued, respecting the diversity of gender, sexuality, age, socioeconomic status, ability, ethnicity, race, and culture. We always welcome suggestions that can help us achieve this goal. Additionally, if any of our class scheduled activities conflicts with religious events, please inform the instruction team so that we can make appropriate arrangements for you.

Students with disabilities: your access to this course is extremely important to us. The institute has policies regarding disability accommodation, which are administered through the Office of Disability Services: http://disabilityservices.gatech.edu. Please request your accommodation letter as early in the semester as possible, so that we have adequate time to arrange your approved academic accommodations.

Office hours and questions

Office hours will start on the second week of clases. Please follow the instruction on this Excel Sheet [Undergrad] [Grad] to signup for a 10-minutes slot with one of the TAs. If you require more than ten minutes, please advise the TAs. They’ll return to your BlueJeans meeting once they have completed their appointments with other students. You just need to add your name, question of interest and your BlueJeans meeting link. Please do not change the other part of the Excel Sheet [Undergrad] [Grad]. The TA meetings are designed to be one-on-one. Please do not join another student’s BlueJeans meeting. The sole exception to this policy being discussions about the project, in which your fellow team members can also join. In-person office hours are only available by appointment and will likely be held outdoors, in line with the aforementioned Georgia Tech's and CDC guidelines with respect to preventing the spread of the coronavirus.


  • 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.
    • You will need to submit all your assignments using Gradescope. Instructions on how to submit your code and written portions will follow with every assignment. Handwritten solutions WILL NOT BE ACCEPTED and you will not receive credit for a handwritten submission.
    • You are required to use Markdown, Latex (watch the tutorial created by our own team [Undergrad Access] [Grad Access] and OverLeaf Latex Example in the Video), or a word processing software to generate your solutions to the written questions. Because handwritten solutions WILL NOT BE ACCEPTED.
    • All assignments follow the “no-late” policy. Assignments received after the due date and time will receive zero credit.
    • [*IMPORTANT] All students are expected to follow the Georgia Tech Academic Honor Code. Because of the large size of our class, if we observe any (even small) similarity\plagiarisms detected by GradeScope or our TAs, WE WILL DIRECTLY REPORT ALL CASES TO OSI, which may unfortunately lead to a very harsh outcome.
    • You can easily export your Jupyter Notebook to a Python file and import that to your desired python IDE to debug your code for assignments.
    • You are NOT allowed to share or discuss ANY assignment codes, information or answers with other students. Edstem is the best place to have discussion regarding assignments and course topics. Discussions can be on a whiteboard level with other students such as high level conceptual questions (i.e. what is independency in Naive Bayes model)
    • We have 4 big assignments in total. The reason we do not call them project, 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)
      • [12.5%] HW1: Linear Algebra, Probability and Statistics, Maximum Likelihood Estimation, Optimization, Information Theory
      • [12.5%] HW2: KMeans, Expectation Maximization, Gaussian Mixture Model, Clustering Evaluation
      • [12.5%] HW3: Singular Value Decomposition, Principal Component Analysis, Linear Regression, Regularization, Naive Bayes
      • [12.5%] HW4: Decision Trees, Random Forest, Support Vector Machine, Neural Networks, CNN

  • Project (30%)

    • Proposal (5%)

      • A project proposal should be written on your GitHub page. It is also a good starter to come up with the first draft of your project.
      • You need to provide us the link to your GitHub page. Make sure your GitHub repository is private.
      • It should be less than 500 words single spaced. References are not the part of the word count.
      • A project proposal should include:
        • Introduction/Background: A quick introduction of your topic and mostly literature review of what has been done in this area. You can briefly explain your dataset and its features here too.
        • Problem definition: Why there is a problem here or what is the motivation of the project?
        • Methods: What algorithms or methods are you going to use to solve the problems. (Note: Methods may change when you start implementing them which is fine)
        • Potential results and Discussion (The results may change while you are working on the project and it is fine; that's why it is called research)
        • At least three references (preferably peer reviewed). You need to properly cite the references on your proposal.
        • Add proposed timeline from start to finish and list each project members' responsibilities. Fall and Spring semester sample.
      • A checkpoint to make sure you are working on a proper machine learning related project. You are required to have your dataset ready when you submit your proposal. You can change dataset later. However, you are required to provide some reasonings why you need to change the dataset (i.e. dataset is not large enough because it does not provide us a good accuracy comparing to other dataset; we provided accuracy comparison between these two datasets). The reasonings can be added as a section to your future project reports such as midterm report.
      • Your group needs to submit a presentation of your proposal. Please provide us a public link which includes a 3 minutes recorded video. I found that OBS Studio and GT subscribed Kaltura are good tools to record your screen. Please make your visuals are clearly visible in your video presentation.
      • 3 MINUTE is a hard stop. We will NOT accept submissions which are 3 minutes and one second or above. Conveying the message easily while being concise is not easy and it is a great soft skill for any stage of your life, especially your work life.
    • Midterm report (10%)

      • A checkpoint to make sure that you have had major progress in your project. You will add information to your project Proposal and turn it into your midterm report.
      • You need to provide us the link to your GitHub page. Make sure your GitHub repository is private.
      • The midterm report does not have a word count limitation.
      • A project midterm report is quite similar to your proposal with the exception of having actual results instead of potential ones:
        • Introduction/Background
        • Problem definition
        • Data Collection
        • Methods
        • Results and Discussion
          • All groups should have their dataset cleaned at this point
          • We expect to see data pre-processing in your project such as feature selection (Forward or backward feature selection, dimensionality reduction methods such as PCA, Lasso, LDA, .. ), taking care of missing features in your dataset, ...
          • We expect to see at least one supervised or unsupervised method implemented and the results need to be studied in details. For example evaluating your predictive model performance using different metrics (take a look at ML Metrics)
      • You do not submit any video recording for the midterm report.
    • Final report (15%)

      • You need to provide us the link to your GitHub page. Make sure your GitHub repository is private.
      • A final report should include:
        • Introduction/Background
        • Problem definition
        • Data Collection
        • Methods
        • Results and Discussion (We expect to see multiple predictive models and your team need to compare them together and evaluate the results. If your team is working on a Deep learning project, you could finely tune hyperparameters and explain how it could improve the results or you could employ different architectures or methods)
        • Conclusions
      • Your group needs to submit a presentation of your final report. Please provide us a public link which includes a 6 to 9 minutes recorded video. I found that OBS Studio and GT subscribed Kaltura are good tools to record your screen. Please make sure your visuals are clearly visible in your video presentation.
      • 9 MINUTE is a hard stop. We will NOT accept submissions which are 9 minutes and one second or above.

    • Sample Projects

    • General project guidance

      • Your project will be graded based on the following criteria:
      • Was the motivation clear?
        • What is the problem?
        • Why is it important and why we should care?
        Were the dataset and approach used effectively?
        • How did you get your dataset?
        • What are its characteristics (e.g. number of features, # of records, temporal or not, etc.)
        • Why do you think your approach can effectively solve your problem?
        • What is new in your approach?
        Were the experiments, results, and conclusion satisfactory?
        • How did you evaluate your approach?
        • What are the results?
        • How do you compare your method to other methods?
        How was the presentation in general?
        • Finished on time?
        • Effective visualizations? (Are they relevant? Do they help you better understand the project's approaches and ideas?)
        • Use of text (Succinct or verbose?)
      • Undergrad students can ONLY team up with Undergrad Students, and Grad students can ONLY team up with Grad students. If you are in a Grad students team, you are required to have both unsupervised and supervised learning in your project. I highly recommend Undergrad students to use both unsupervised and supervised learning in your project. However, if you were to pick one, please go with supervised learning. You can not team up with other sections of this class such as CS-4641-B.
      • In order for you to obtain hands-on experience applying the topics covered in this course, you are expected to complete a term project utilizing real-world data. The project will encompass both unsupervised and supervised learning.
      • Each project needs to be completed in a team of five people (you will be forming your team on your own. In case you cannot find a team, we will randomly assign you a team). Team members need to clearly claim their contributions in the project report. Once your teams have been formed and you have selected a topic, you will be assigned a mentor, who will provide you with general guidance on your project. It is important to note that your team will lead the project effort: obtaining the data, researching data-driven approaches to accomplish your project goal and coordinate your own activities. The role of the mentor is solely to advise you, should you find yourself stuck and unable to make progress. We also accept a team of four, if you really cannot find the fifth team member.
      • You will create a GitHub page page for your project, which you will use to publish your main deliverables. There will be three deliverables published to your GitHub: a proposal, a midterm checkpoint, and a final report.
      • Seminars: To help you conduct your project successfully, We have project seminars where one or two TAs will present their ML projects, prior students' projects, research or industrial projects. Doing so, you will gain a good sense of what it is being done in both Academia and Industry. Besides that, students can ask general questions about their class project and how to improve that in each seminar. Seminars will be streamed online and recorded and they will be published on the course website. Similar to the class lectures, Please ensure that you join to these seminars and get yourself familiar with the practical and real-world application of ML. We will have Edstem post for each seminar, its exact time, and joining information.
      • Google colaboratory allows free access to run your Jupyter Notebook. I strongly suggest you use it for your project, especially for teams that are going to employ Deep Learning. Don't forget to take advantage of Google Cloud Platform and AWS Educate as well.

  • Quizzes (15%)

    • There will be 14 quizzes throughout the semester.
    • We will consider your top 10 quizzes' scores. Each quiz will have 1.50% of your final score.
    • [*IMPORTANT] All quizzes are mandatory to be taken even if they do not count toward your final grade. If you miss a quiz, we will consider that as one of your 10 quizzes' scores which counts as ZERO. Let's say you miss taking one quiz, we will consider your top 9 quizzes' scores + one zero score. Therefore, you will lose 1.50%.
    • The topic of each quiz will coincide roughly with the content covered in class on that week.
    • Quizzes will have a duration of seven-minutes for Undergrad students and six-minutes for Grad students. Each quiz will have five multiple choice questions. They will be available from 12:00 am AOE Monday until 11:59 pm AOE Tuesday. Quizzes will be moved to Wednesdays in case there are school\national holidays on Mondays. Any possible changes on quizzes dates will be reflected on our course schdule page. Please make sure to check our class website before taking the quiz.
    • Quizzes measure your understanding of the topics and they will be mostly conceptual questions.
    • Quizzes' answers will be released as soon as all our students took them including our ODS students. Please do not ask any questions about a quiz that you just take on Edstem before we release the answers.
    • Quizzes questions are selected randomly from our question bank, which means that students will not receive the same questions for their quiz.

  • Class participation (5%)

    • Edstem has statistics which give us many measurements regarding how much a student has been involved on Edstem's activities such as viewing posts, answering questions, asking questions and so on. We use this to account for your Class Participation score. We also will add class attendance to this score. At the end of the semester, we will define a minimum and maximum number of involvement considering all the students and your grade will be defined based on that.
    • We will RELEASE the class participation score on the last day of the class when we have all the score for projects, quizzes and assignments. If you ask us what is my participation score before the last day of the class; we will say we do not know. So please be patient.

  • Bonus points (up to 8%)

    • About bonus points: Bonus points will be counted to always be beneficial for your final grade. More information on bonus points for assignments will be provided as the semester progresses. If it becomes necessary to curve grades, bonus points will be applied after curving, not before.
    • Undergrad and grad: You can obtain up to 5% bonus points by answering the challenging questions we may have in some of the HWs.
    • Undergrad: You will notice that we have bonus points for all the hws, where grad students are required to answer those questions, but it will be optional for undergrad students. You will receive up to 3%, if you answer those questions. Note that these are different than the challenging questions. Challenging questions are bonus for both grad and undergrad.
    • How does it work? For example, hw 1 may have 30 bonus points, hw 2 may have 20 bonus points and so on. If you receive all the bonus points for all your hws, we will add 5% to your final grade. If you are an undergrad and you answer all the challenging and Grad students questions, you will receive 8%.
    • Note and Example: There's a cap to how much extra credit you can get, so it is (bonus points earned)/(total bonus points available throughout the entire semester). Let's say by the end of the semester there was a total of 100 bonus for all points (100 is just a number we are randomly choosing here) between hw1, hw2, hw3, hw4, and you earned 20 bonus for all points for the whole semester, then at the end of the semester your grade will be bumped up by 5% * 20/100 = 1% from the bonus for all points. The calculation is similar for the 3% bonus for undergrad points.

  • Grade Calculator

    • Grade calculation can be slightly complicated considering we have different types of bonus questions. Our last semester students created this Grade Calculator Excel Sheet. Please give it a try to calculate your grade along the way.

COVID-19 Policy

This semester is challenging due to the ongoing Covid-19 pandemic and a growing awareness of inequities.  Please review the most up-to-date information relates to specific services and guidelines for courses during this semester at TECH Moving Forward website and in the Academic Restart Frequently Asked Questions.  


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:

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

Dataset Ideas (may need API, or scraping) - Thanks to Polo and everyone who contributed with suggestions to these datasets