CX4240: Introduction to Computational Data Analysis (2022 Spring)
Table of Contents
Logistics
- Lecture time: Mons and Weds, 3:30pm-4:45pm
- Location: J. Erskine Love Manufacturing 185 (also streamed at bluejeans.com/5341114422)
- Instructors: Chao Zhang
- Teaching Assistant: Brad Baker <bbradt@gatech.edu> and Binghong Chen <binghong@gatech.edu>
- Office Hours:
- Instructor: Mons 2-3 @ bluejeans.com/5341114422
- TA Office Hour: Weds 2:30-3:30pm @ bluejeans.com/547687219/6944
- Piazza: https://piazza.com/gatech/spring2022/cx4240
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, calculus, and linear algebra; 2) basic knowledge of machine learning; 3) solid programming skills, preferably in Python.
Schedule
| Date | Topic | Due |
|---|---|---|
| 01/10/2022 | Course Overview | |
| 01/12/2022 | Probability and MLE | Piazza Signup |
| 01/17/2022 | No Class (Martin Luther King Day) | |
| 01/19/2022 | Data Analysis Toolbox | |
| 01/24/2022 | Linear Regression | |
| 01/26/2022 | Linear Regression | HW1 Out |
| 01/31/2022 | Example Projects | |
| 02/02/2022 | Naïve Bayes Classifier | |
| 02/07/2022 | Logistic Regression | |
| 02/09/2022 | Neural Networks | HW1 Due |
| 02/14/2022 | Neural Networks | Project checkpoint 1 signup |
| 02/16/2022 | Project checkpoint 1 & discussion | HW2 Out |
| 02/21/2022 | CNNs and RNNs | |
| 02/23/2022 | Decision Trees | |
| 02/28/2022 | Random Forest | |
| 03/02/2022 | Clustering Analysis and K-Means | HW2 Due |
| 03/07/2022 | Hierarchical Clustering | Project checkpoint 2 signup |
| 03/09/2022 | Gaussian Mixture Model | HW3 Out |
| 03/14/2022 | Dimension Reduction | |
| 03/16/2022 | Project checkpoint 2 & discussion | |
| 03/21/2022 | No Class (spring break) | |
| 03/23/2022 | No Class (spring break) | |
| 03/28/2022 | Application: Text Representation | |
| 03/30/2022 | Application: Text Embedding & Clustering | HW3 Due |
| 04/04/2022 | Application: Text Classification | project presentation signup |
| 04/06/2022 | Review Class | |
| 04/11/2022 | Exam | |
| 04/13/2022 | No Class (Project Preparation) | |
| 04/18/2022 | ||
| 04/20/2022 | Project Presentation | project presentation due |
| 04/25/2022 | presentation peer grading due |
Grading
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 April 11 in lieu of the regular class:
- The exam will be open-book. However, no peer communication is allowed—you may not message or collaborate with others.
- There will be no make-up exams. You will get zero credit for your missed exam.
Resources
- The Elements of Statistical Learning, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- 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
- 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
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, by Aurélien Géron
Other resources, such as machine learning toolboxes and datasets, will be provided throughout the course.