Advanced Machine Learning for Data Science (362-0-20)
Instructors
Lizhen Shi
Meeting Info
University Hall 122: Tues, Thurs 3:30PM - 4:50PM
Overview of class
The Advanced Machine Learning course is designed to equip students with a comprehensive understanding of both the mathematical theory and practical implementation of various Machine Learning (ML) and Deep Learning (DL) models. Building on the sequence course, topics covered include multi-class classification, unsupervised learning, and deep neural networks. Students gain hands-on experience with various popular ML/DL models for analyzing unstructured data.
Registration Requirements
STAT 303-1,2,3 or STAT 301-1,2,3
Learning Objectives
At the completion of this course, students should be able to:
- Extend their knowledge from binary classification to multi-class classification
- Master various clustering algorithms in unsupervised learning
- Develop proficiency in reducing the dimensionality of data and recognizing when it is necessary to do so.
- Understand Deep Learning theory and how to implement them for analyzing unstructured data
Teaching Method
Each class will be divided into lecture and work time on an in-class assignment. Concepts and theory will be introduced in the lecture part and after that, students will work on their in-class assignments with the help of the Tas and the instructor present. The students are encouraged to ask questions and collaborate during the in-class work time.
Everyone must bring their own laptop to each class, as coding in Python will be required. Installation of Anaconda Navigator is necessary.
Evaluation Method
In-class assignments (10%)
Homework assignments (40%)
Prediction problem (20%)
Final exam (30%)
Class Materials (Required)
The course does not have any textbooks. The main content will be taught and uploaded in lecture notes. Any useful parts/exercises from various books will be uploaded as supplementary material.
Everyone must bring their own laptop to each class, as coding in Python will be required. Installation of Anaconda Navigator is necessary.
Class Attributes
Formal Studies Distro Area
Enrollment Requirements
Enrollment Requirements: Prerequisites: STAT 301-3 or STAT 303-3.
Add Consent: Department Consent Required