Advanced Machine Learning for Data Science (362-0-20)

Instructors

Lizhen Shi

Meeting Info

Frances Searle Building 2107: Mon, Wed 9:30AM - 10:50AM

Overview of class

Building on the sequence (STAT303/301), this course delves into advanced topics in machine learning. Students will explore essential areas such as gradient descent, vectorization, multiclass classification, unsupervised learning, and deep learning. Practical implementation using NumPy in Python will include implementing gradient descent and constructing custom deep neural networks, enabling students to gain a comprehensive understanding of these fundamentals in ML. Through hands-on labs analyzing diverse datasets, students will develop robust practical skills in handling unstructured data. Emphasizing both theoretical knowledge and real-world application, this course prepares students to effectively tackle complex challenges in the field.

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 seamlessly from binary to multi-class classification.
- Excel in utilizing a variety of widely-adopted clustering algorithms in unsupervised learning scenarios.
- Cultivate expertise in dimensionality reduction techniques, proficiently visualizing high-dimensional data in 2D/3D spaces.
- Acquire a comprehensive understanding of Deep Learning principles and methodologies, enabling the effective analysis of unstructured data such a as images and sequences through practical implementation.

Teaching Method

The instructor will dedicate most of the lecture time to concepts and theory. To connect theory with programming, there will be an after-class coding quiz associated with each lecture. This coding quiz is designed to provide hands-on experience and is due before the next lecture. After completing the attendance quiz for each lecture, you will receive a zip file containing the dataset and notebook for the coding quiz.

Everyone must bring their own laptop to each class, as coding in Python will be required. Installation of Anaconda Navigator is necessary.

Evaluation Method

After-class assignments (33%)
Homework assignments (50%)
Midterm Exam
Final paper (20%)
Participation (4%)

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: Registration in this course is reserved for Data Science Majors only Prerequisites: STAT 301-3 or STAT 303-3.