CX4240: Introduction to Computational Data Analysis (2021 Spring)
Table of Contents
Logistics
- Lecture time: Mons and Weds, 3:30pm-4:45pm
- Location: https://bluejeans.com/5341114422
- Instructors: Chao Zhang
- Teaching Assistant: Brad Baker <bbradt@gatech.edu> and Yue Yu <yueyu@gatech.edu>
- Office Hours:
- Instructor: Weds 2:30-3:20pm @ bluejeans.com/5341114422
- TA Office Hour: Mons 2:30-3:20pm @ bluejeans.com/881278440
- Piazza: https://piazza.com/class/kjkdjm2rxza2ps
- Piazza will be the main place for course discussions and announcements. If you have questions, please ask it on Piazza first because 1) other students may have the same question; 2) you will get help faster than emails.
- If it's something you do not like to discuss publicly on Piazza, send an email with CX4240 in the subject.
Course Content
Q: What will be covered in this course? A: This course introduces techniques for computational data analysis, with an emphasis on machine learning algorithms and their applications to real-world data. On the technique side, we will cover key supervised machine learning methods (linear regression, logistic regression, neural networks, tree-based models) and unsupervised method (k-means, Gaussian mixture models, expectation-maximization, dimension reduction). On the application side, it will introduce various applications of these techniques, particularly on text data analysis and natural language processing. It will introduce how to formulate real-world tasks as data analysis problems, key methods for solving these problems, and their advantages and disadvantages.
Q: Who will benefit from this course? A: The learning objective is that by the end of this course, the students are able to formulate their data analysis problems at hand, choose appropriate computational models to acquire insights from data automatically, and even come up with innovative solutions for solving open problems in this field. The course will be helpful for students who want to solve practical problems using machine learning and data science techniques. The course will provide useful techniques for students who want to do edge-cutting research in data mining, machine learning, natural language processing, and others.
Q: What are the prerequisites? A: Prerequisites for this course include 1) solid knowledge of probability, statistics, and linear algebra; 2) basic knowledge of machine learning; 3) solid programming skills, preferably in Python.
Schedule
Date | Topic | Due |
---|---|---|
01/18/2021 | No Class (Martin Luther King Day) | |
01/20/2021 | Course Overview | Piazza Signup |
01/25/2021 | Math Basics I | |
01/27/2021 | Math Basics II | HW1 Out |
02/01/2021 | Data Analysis Toolbox | |
02/03/2021 | Example Projects | |
02/08/2021 | Linear Regression | HW1 Due |
02/10/2021 | Linear Regression | |
02/15/2021 | Naïve Bayes and Logistic Regression | HW2 Out |
02/17/2021 | Support Vector Machine | |
02/22/2021 | Neural Networks | |
02/24/2021 | Neural Networks | |
03/01/2021 | CNNs and RNNs | HW2 Due |
03/03/2021 | Decision Trees | |
03/08/2021 | Ensemble Methods and Random Forest | HW3 Out |
03/10/2021 | Midterm Review | |
03/15/2021 | Midterm Exam Day | |
03/17/2021 | Clustering Analysis and K-Means | |
03/22/2021 | Hierarchical Clustering | HW3 Due |
03/24/2021 | No Class (Mid-Semester Break) | |
03/29/2021 | Gaussian Mixture Model | HW4 Out |
03/31/2021 | Dimension Reduction | |
04/05/2021 | Application I: Text Embedding | |
04/07/2021 | Application II: Text Classification | |
04/12/2021 | Review Class | HW4 Due |
04/14/2021 | Project Presentation and Peer Grading | presentation video due 04/13 |
04/19/2021 | Project Presentation and Peer Grading | |
04/21/2021 | Final Exam Day | |
04/26/2021 | No Class | project report due |
Grading
Homework (40%)
There will be four assignments, each account for 10% towards your final score. Each assignment could be either programming or written analysis for testing your understanding of the taught content.
- Late policy: Assignments are due at 11:59PM of the due date. You will be allowed 2 total late days without penalty for the entire semester (for homework only, not applicable to exams or projects). Once those days are used, you will be penalized according to the following policy:
- Homework is worth full credit before the due time.
- It is worth 75% credit for the next 24 hours.
- It is worth 50% credit for the second next 24 hours.
- It is worth zero credit after that.
- Follow the Georgia Tech Academic Honor Code.
Project (30%)
You need to complete a project on using computational data analysis techniques to tackle a real-life data analysis problem. Each project needs to be completed in a team of 2-4 people. Here are some guidelines and resources for doing your project smoothly.
The breakdown of the 30% project score is as follows:
- Project presentation (10%)
- Every team need to make a YouTube video presentation for your project and post the link on canvas
- The deadline for uploading the link to your video is 04/13 11:59pm ET; we will create pages for submitting video links on Canvas.
- Project report (10%): you need to write up a final report for your project and submit on Canvas by 04/26 11:59pm. Here are some instructions and templates for the final project report
- Project peer grading (10%): you need to grade 10 project presentations of other teams, each will count for 1 point. Please use this Google form for peer grading and see here some FAQs about peer grading
Midterm Exam (15%)
We have a take-home midterm exam on March 15 in lieu of the regular class.
- The midterm exam will be a take-home and open-book exam. However, no peer communication is allowed—you may not message or collaborate with others, and that includes posting questions or answers on websites during the exam period.
- There will be no make-up exams. You will get zero credit for your missed midterm exam.
- We will release the detailed instructions before the exam.
Final Exam (15%)
We have a take-home final exam on April 21 in lieu of the regular class.
- Similar to the mid-term exam, the final exam will be a take-home and open-book exam. However, no peer communication is allowed—you may not message or collaborate with others, and that includes posting questions or answers on websites during the exam period.
- There will be no make-up exams. You will get zero credit for your missed midterm exam.
- We will release the detailed instructions before the exam.
FAQs
- For remote lectures, will there be recordings?
- Yes, for every remote lecture and paper presentation, we will record on Bluejeans and shared a link after class.
Resources
- Machine learning, by Tom Mitchell
- Pattern recognition and machine learning, by Christopher Bishop
- 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
- Dive into Deep Learning, by Aston Zhang, Zack C. Lipton, Mu Li, and Alex Smola
Other resources, such as machine learning toolboxes and datasets, will be provided throughout the course.