CS 460G – Machine Learning

https://aaz-imran.github.io/teaching/2026-spring-cs460g

Syllabus

Time: TR 9:30 AM – 10:45 AM

Course Instructor: Dr. Abdullah-Al-Zubaer Imran
Office: 319 Marksbury
Office Hours: Tuesdays 11 AM - 1 PM

Course Description:
Study of computational principles and techniques that enable software systems to improve their performance by learning from data. Focus on fundamental algorithms, mathematical models and programming techniques used in Machine Learning. Topics include: different learning settings (such as supervised, unsupervised and reinforcement learning), various learning algorithms (such as decision trees, neural networks, k-NN, boosting, SVM, k-means) and crosscutting issues of generalization, data representation, feature selection, model fitting and optimization. The course covers both theory and practice, including programming and written assignments that utilize concepts covered in lectures.

Course Outcomes
This course will help students achieve the following educational objectives:

• Understand the principal techniques and computational principles for enabling computers to learn from data
• Develop computer programs that manipulate different types of data (such as images, texts, videos, etc.)
• Analyze complex machine learning problems and apply principles of computing to identify appropriate solutions
• Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of a machine learning problem
• Apply computer science theory, machine learning fundamentals, and software development fundamentals to produce computing-based solutions
• Effectively communicate about machine learning models or concepts in a variety of professional contexts

Prereqs:

• Strong programming ability (CS 315)
• Basic probability and statistics (STA 281)
• Basic concepts of linear algebra (MA/CS 321 or MA/CS 322)

Required books
There is no required textbook for CS 460G.

Recommended books

• Pattern Recognition and Machine Learning
  Christopher M. Bishop, Springer, 2006
• Deep Learning: Foundations and Concepts
  Christopher M. Bishop, Springer, 2024
• Machine Learning
  Tom M. Mitchell, McGraw-Hill, 1997
• Python Machine Learning
  Sebastian Raschka, Packt, 2015
• Machine Learning: A Probabilistic Perspective
  Kevin P. Murphy, MIT Press, 2012
• Deep Learning
  Ian Goodfellow, MIT Press, 2016

Course Schedule/Outline (Tentative):

Course Activities:

• Class participation (10%) – lecture quizzes, in-class and Canvas activities
• Assignments (30%) – Four programming and written assignments (only the top 3 will count)
• Project (40%) – Final group project (proposal, intermediate milestones, demo presentation, code, and report)
• Assessments (20%) – In-class written Exam

Grading Scale:
After the final percentage grade is calculated, the following scale will be used to determine the final letter grade.

For undergraduate students:

• 90–100% (A)
• 80–89% (B)
• 70–79% (C)
• 60–69% (D)
• 0–59% (E)

For graduate students:

• 90–100% (A)
• 80–89% (B)
• 70–79% (C)
• 0–69% (E)

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Attendance Policy: Students are required to attend every lecture. Students only present in the class can take the associated quiz and get the available points. Missing a maximum of 2 lectures will be excused. Any student missing more than 2 lectures without any reasonable excuses will start losing 10 points for every absence.

Academic Integrity: Please strictly follow the Academic Offenses Rules (plagiarism, cheating, and falsification or misuse of academic records). Also, keep in mind that any copyrighted materials (e.g., images and other media), and published contents (e.g., academic papers, books, web sources, online tools) used in your submissions and project should be properly cited. Ideas from people other than your own (for the project—ideas from outside your group) should be acknowledged.

Late Policy: Late submissions (assignment, project proposal, code, project final report) will be subject to a 1% score penalty per hour post-deadline. A score of 0 will be automatically assigned for any submissions made four days after the deadline. Late submissions will be accepted only for certain circumstances (e.g., medical emergency) with proper evidence.
Exceptions to this rule may be requested by providing appropriate documentation which will be considered at the discretion of the instructor.

Generative AI Policy: GenAI tools such as ChatGPT may be used in this course for the purposes of pre-submission activities. However, students are responsible for submitting work that meets the assignment standards for quality and factual accuracy. Check the Canvas page for more detailed instructions on this. If you have any questions or concerns about the policy, contact the instructor before submitting any assignments.

Disability and Special Accommodation: Please let the instructor know of any needed accommodation in the first two weeks. Please also see Academic Accommodation for further assistance.

Academic Policy Statements, Resources Available to Students

Useful Resources:

• UK Libraries
• Free tutoring and coaching at UK
• Google Scholar
• Image Datasets
• PyImageSearch