CX4240: Introduction to Computational Data Analysis (2024 Spring)

Table of Contents


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 machine learning methods (linear regression, logistic regression, neural networks, tree-based models) and self-supervised learning for foundation models. 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, calculus, and linear algebra; 2) basic knowledge of machine learning; 3) solid programming skills, preferably in Python.


Date Topic Due  
  Module 1: Background    
01/08/2024 Course Overview    
01/10/2024 Probability and MLE Piazza Signup  
01/15/2024 No Class (Martin Luther King Day)    
01/17/2024 Data Analysis Toolbox    
  Module 2: Linear Models    
01/22/2024 Linear Regression    
01/24/2024 Linear Regression HW1 Out  
01/29/2024 Example Projects    
01/31/2024 Logistic Regression    
02/05/2024 Naïve Bayes Classifier    
02/07/2024 Feature Design and Learning for Text    
02/12/2024 Feature Design and Learning for Text    
  Module 3: Neural Networks    
02/14/2024 Neural Networks HW1 Due  
02/19/2024 Project checkpoint & discussion HW2 Out  
02/21/2024 Neural Networks    
02/26/2024 CNNs and RNNs    
02/28/2024 Transformers    
  Module 4: Tree Models    
03/04/2024 Decision Trees HW2 Due  
03/06/2024 Random Forest HW3 Out  
03/11/2024 Midterm Exam    
03/13/2024 Project checkpoint & discussion    
03/18/2024 No Class (spring break)    
03/20/2024 No Class (spring break)    
  Module 5: Large Language Models    
03/25/2024 Large Language Model (LLM)    
03/27/2024 LLM Instruction Fine-Tuning HW3 Due  
04/01/2024 LLM Alignment    
04/03/2024 LLM Agents and Decision Making Project presentation signup  
04/08/2024 Project checkpoint & discussion    
  Module 6: Projects    
04/10/2024 Project presentation    
04/15/2024 Project Presentation    
04/17/2024 Project Presentation    
04/22/2024 Project Presentation    
04/24/2024 No Class (Reading Day)    
04/28/2024 Project Report Due Project Report Due  


Homework (30%)

There will be three assignments, each account for 10% towards your final score. Each assignment includes written analysis and/or programming 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 (48 hours) 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.

Exam (40%)

One exam will be held on March 11 in lieu of the regular class:

  • It will be a closed-book exam, so no notes or communication with peers is allowed.
  • There will be no make-up exams, so be sure to attend on the scheduled date. Missing the exam will result in zero credit.


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