• Lecture time: Mons and Weds, 3:30pm-4:45pm
• Location: J. Erskine Love Manufacturing 185
• Instructors: Chao Zhang
• Teaching Assistant: Yue Yu <[email protected]> and Lingkai Kong <[email protected]>
• Office Hours:
  • Instructor: Mons 2-3pm @ https://gatech.zoom.us/j/91570480132
  • TA Office Hour: Weds 5-6pm @ https://gatech.zoom.us/j/4595635754
• Piazza: https://piazza.com/gatech/spring2024/cx4240

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.

• 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.