DS675.005 - Introduction to Machine Learning (FALL · 2023)

• Description
• Teaching Team
• Logistics
• Schedule

Description (DS675.005)

Machine Learning develops computer programs that can improve their performance by tapping into existing data and taking feedback from the environment. Systems based on ML have already exceeded human performance in several tasks, including image classification and games like Chess and Go. ML has also made leaps in even more complicated tasks, like Natural Language Processing or self-driving vehicles, and it has even produced art that imitates the style of human artists! This course offers an intense introduction to the fundamental ML concepts and algorithms that constitute the core of these spectacular developments. It takes you on a tour from the basic mathematical notions and algorithms to some of the recent developments, e.g., deep convolutional neural networks, recurrent neural networks, and transformers. You will gain exposure to cutting-edge ML development tools such as Jupyter notebooks, scikit-learn, and PyTorch via hands-on assignments and projects that will instill a working and immediately applicable knowledge of ML methods and will prepare you for more advanced ML courses.

Teaching Team

Logistics

• Time: Friday, 2:30pm - 5:20pm EST
• Location: ECEC 115
• Mode: Face-to-Face
• Discussions: Feel free to participate in different discussion forums on Canvas
• Quizzes & Assignments: submit and post on Canvas
• Contact: For external inquiries, personal matters, or emergencies, please email the instructor or TA directly.
• Prerequisites: The course does not have other course prerequisites. Background on some basic calculus, linear algebra, probability, and programming ability is required. The following free online materials are recommended for reviewing this background:
  • Mathematics for Machine Learning
  • A visual guide to NumPy

Grading

• Homework [30%]. Six homework assignments of equal grading weight. The weakest of the six grades is dropped from the calculation.
• Interview-style Quizzes [10%]. Weekly Canvas quizzes reinforcing the material of each module.
• Class Participation [10%]. You are expected to attend classes and participate in classes by listening and understanding class contents and asking related questions. You are also expected to participate in weekly Canvas discussions prompted by the instructor, with meaningful questions and answers related to the week’s topics or assignments.
• Midterm Exam [15%]. In-person exam, 80 minutes. Students are expected to bring a fully charged laptop, as the exam will be on Canvas with LockDown browser. Each student is allowed to bring at most 5 pages of notes. In the event the exam has to take place online, Respondus Monitor will be used for proctoring.
• Final Exam [15%]. Cumulative, 120 minutes. Otherwise, similar to midterm exams.
• Mini-Project [20%]. You are expected to complete a mini-project in a group. The project will consist of four milestones with weights: [4%, 1%, 5%, 10%]. More details please check the project requirement document link.

Schedule

The lecture slides are available for download in the Google Drive folder, while all weekly quizzes and biweekly assignments can be accessed on Canvas.

Week	Date	Lecture	Logistics
Week 1	9/8	Introduction and overview	Q1, H1 posted
Week 2	9/15	Basics of Python, Linear regression and Gradient Descent	Q2, H1 due, H2 posted
Week 3	9/22	Linear Classification and Perceptron	Q3
Week 4	9/29	Generative Models and Logistic regression	Q4, H2 due, H3 posted
Week 5	10/6	SVM and Decision Trees	Q5
Week 6	10/13	Ensemble and Boosting	Q6, H3 due, h3 posted
Week 7	10/20	Unsupervised Learning	Q7
Week 8	10/27	Midterm exam	No quiz
Week 9	11/3	Artificial Neural Networks	Q9, h3 due, H5 posted
Week 10	11/10	Autoencoders	Q10
Week 11	11/17	Convolutional Neural Networks	Q11, H5 due, H6 posted
Week 12	11/24	Recurrent Neural Networks	Q12
Week 13	12/1	Transformers & LLMs	Q13
Week 14	12/8	Project presentations & Review	H6 due
Week 15	12/20	Final exam

*The schedule is subject to change. Please check Canvas for the most up-to-date information.

For more detailed course information, please refer to the syllabus and the Canvas modules!