CSD 42 |

Introduction to Machine Learning

Course Description

The class will offer a comprehensive foundation in machine learning models and their application in making predictions.

It will cover both the theoretical underpinnings and practical uses of machine learning, progressing systematically through various topics such as prediction and classification challenges, kernel techniques, generative modeling, probability and optimization theories, representation learning, neural networks, and more.

Throughout the course, students will also become aware of the limitations inherent in machine learning and strategies for enhancing these limitations with improved approaches.

The course materials, including slides and project details, have been drawn from prominent figures in the field such as Andrew Ng, John Guttag, Sanjoy Dasgupta, Tom Mitchell, Frank Ferraro, Hal Daume III, Trevor Hastie, Robert Tsibrani, and Roger Grosse.


  • Instructor Manas Gaur


    Office Hours: Tuesdays and Thursdays: 12 PM to 1 PM; Room: 337 ITE Building

  • Teaching Assistant


    Office Hours: Tuesdays and Thursdays, 3:00 PM to 4:00 PM (Rm 344, ITE Building)


  • Mid-term Date and Time and Course Syllabus
  • - Mid-term (November 6 to November 12); Lecture 5 upto Perceptron Mistake Bound
  • Are we allowed to use ChatGPT?
  • - You are allowed to use ChatGPT as long you take it as an assistive tool.
  • Is there any late policy?
  • - You have a total of 6 slip days that you can apply to your semester's homework. We will simply not award points for any late homework you submit that would bring your total slip days over six. If you are in the Disabled Students' Program and you are offered an extension, even with your extension plus slip days combined, no single assignment can be extended more than 5 days. (We have to grade them sometime!)
  • Are we referring to any books?
  • - Look at useful reading here https://manasgaur.github.io/CMSC678_F23/resources
  • Is there is a possibility of attending the class virtually?
  • - Yes. It should be used only in unforseen circumstances. Link to join virtually- https://umbc.webex.com/meet/manas
  • Where can I find the recordings of the lectures?
  • - They will be present either on this website (under resources) or blackboard.
  • What is the link to the student communication channel?
  • - We will be using discord server for student communication. The link is https://discord.gg/HwewjQGF . The channel will be monitored by the instructor and TA, so please do not share illicit content or demonstrate violent/non-polite behavior. It will considered as Academic Dishonesty.

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