Machine Learning: Tools and Algorithms
(CSE 584)
Revised on 8/22/2024
Tue & Thu
10:35AM - 11:50AM
Walker Building 012
Instructor: Prof. Wenpeng Yin, wenpeng@psu.edu
Office hours: 8:30-9:30am on Thursdays
TA: Ibraheem Muhammad Moosa (ibraheem.moosa@psu.edu)
Course Intro
&
Objectives
The course requires proficiency in Python programming and a solid foundation in the following areas: i) linear algebra, calculus, basic probability, statistics, and ii) fundamental machine learning models, including logistic regression, SVM, convolutional neural networks, recurrent neural networks, and Transformers. As a graduate-level machine learning course, it covers both classic machine learning topics, such as Graphical Models and Approximate Inference, and cutting-edge advances, including Deep Reinforcement Learning, Self-Supervised Learning in Large Language Models, and Federated Learning. The course objectives are threefold:
To provide a deeper understanding of foundational machine learning concepts.
To stay current with the latest advancements in the field, particularly those driven by Deep Learning and Large Language Models.
To equip students with both theoretical insights and practical skills, preparing them for a future research career.
There are no required textbooks for this course, but some chapters of the following books are recommended for reading during this course:
Chris Bishop, “Pattern Recognition and Machine Learning”, 2006
Mohri et al., “Foundations of Machine Learning”, 2018
Class Materials
Grading Policy
Your final grade for the course will be based on the following:
Attendance: 10%
Bi-weekly quizzes: 10% (4 Quizzes)
Homework: 20% (2 homeworks in total)
Midterm project: 30%
Final project: 30%
Grades will be assigned on the following scale:
93+: A
90-92: A-
87-89: B+
83-86: B
80-82: B-
75-79: C+
70-74: C
60-69: D
59 and below: F
Schedule
This schedule is subject to change throughout the semester. Changes will be communicated in class and through Canvas.
If you need CSE-specific compute resources, please refer to this link: https://www.eecs.psu.edu/cse-student-lab-access/index.aspx
Please send me an email about your absence (w/ proof if you have) before the class. Explanation after the class is unacceptable.
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