CSD 42 |



{Broad Topics}
Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcment Learning, Active Learning, Explainable Machine Learning

Introduction to Machine Learning, Learning Problem, Generative Modeling, Linear Regression and Probability Estimation, Optimization and Geometry, Support Vector Machine and Kernel Methods, Trees and Forest, Probabilistic Graphical Models, Learning Theory, Representation Learning (Some Excursion to Deep Learning) and Interpretable Method (LIME/SHAP).


{Recommended Materials}
A Course in Machine Learning (CIML) is the book we will referring in this course.

Template for the Final Project

Please choose one of the following templates to submit the final project report:
  • Overleaf NeurIPS Template
  • Overleaf ACM Official Template
The assessment process of the final project is broken down into following milestone:
  • Project information (10 points): Group formation and tentative project title are required fields in the Excel sheet (Check FAQs).
  • Project checkpoint 1 (15 points): For this milestone, it's essential that you've acquired the data and possibly conducted some preliminary data preprocessing. You'll need to deliver a 5-minute presentation summarizing your progress up to this point, including descriptive statistics about the dataset, and outlining your strategy leading up to the next milestone.
  • Project checkpoint 2 (30 points): Your task includes providing a two-page report that outlines the motivation of the project, hypothesis, plan for achieving a plausible answer to the hypothesis, initial milestone (briefly), the modifications made since the initial milestone, any obstacles encountered, and your roadmap for the remainder of the semester.
  • Final Report and Code (45 points): Your final report, with a maximum length of ten pages, should follow a structure similar to that of a concise research paper. It should encompass the following elements: (a) A project overview. (b) An exploration of the significant concepts you delved into. (c) Utilization of concepts from the course. (d) Key takeaways and lessons learned. (e) A comprehensive summary and discussion of your results. (f) If you had additional time, how would you extend and develop the project further? Your final report should accompany a well commented and easy to execute code.
If you have any questions, please email: manas@umbc.edu

Late Policy

  • A total of 6 days are granted.
  • 50% credit for 24 hours after late days
  • Late days are for unforeseen situations (interviews, conference, etc.), do not include them in your plan

Academic Integrity

I take academic dishonesty seriously. Any sort of activity which relate the cheating, plagiarism, fabrication (e.g. use Adobe tools), or copy-paste will be not be tolerated. If caught for the first time would lead to a 50% reduction in the grade on the assignment. If the same activity is performed next, would result in an F grade in the course and a disciplinary action will be taken which may lead to suspension or dismissal. Please do visit the academic dishonesty policy available in the Academic Integrity at UMBC. Especially for computer science classes, there are generally questions about what is and is not allowed. However, you may not write or complete assignments for another student; allow another student to write or complete your assignments; pair program; copy someone else work; or allow your work to be copied. This list is not inclusive.

Grading Policy

  • 5 Homework Assignments (20 % of total grade)
  • Bi-weekly quizzes (10 % of total grade)
  • 1 Midterm (20% of total grade)
  • Reading Assignments (10% of total grade)
  • Final Project [Executable Code and Report] (35% of total grade)
  • Class Attendance and Participation (5% of total grade)
      A (90 and above), A- (85-90), B (75-85), B- (70-74), C (60-69), C- (55-59), F (54 and below)

      Bonus Grades For Final Project

      The bonus grades will be awarded based on the final projects competency to be submitted to a machine learning, data science, or natural language processing conference. To decide on the bonus grades, I would lay emphasis on:
      • Motivation and Problem Formulation,
      • Literature Review,
      • Experiment and Dataset Design,
      • Results and Discussion,
      • Conclusion (What have been achieved, Limitations, and Future Work)

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