SUBHANKAR MISHRA
ଶୁଭଙ୍କର ମିଶ୍ର
Faculty, School of Computer Sciences, NISER
ପାଠକ-ଏଫ, ସଂଗଣକ ବିଜ୍ଞାନ ବିଦ୍ୟାଳୟ, ନାଇଜର
CSE660 - Machine Learning 2024-25 Odd Semester
Teaching Assistants
Syllabus
- Learning, Inductive Bias, Features, Labels, Basics of Statistics and Probability
- Supervised Learning - Some models
- Ensemble Methods: Bagging, Boosting. Learning Theory
- Unsupervised Learning - Some models
- Reinforcement Learning Basics (if time permits)
Expected skills (No mandatory prerequisite)
- Algorithms (CS301 or equivalent)
- Python
- Probability and Statistics
Grading scheme
Grading will be absolute.
- Assignment - 30 marks
- Quiz - 10 marks
- Midterm - 20 marks [may include programming]
- Endterm - 40 marks [may include programming]
- Total -100 marks
Assignment
Submission guidelines:
- Clone GitHub
- index.html
- If this is the first time, create a new row in the table, add your members, project title and
initialize the groups slides and reports.
- You will add/update the links associated with your group.
- Projects folder
- If this is the first time, create a new folder under projects folder
- Add/update your files in that folder only. Do not make any changes other than your group/individual
folder
- Create a pull request after the changes are done
Books
Some recommended books on Machine learning.
- The Elements of Statistical Learning Link
- Mathematics for Machine Learning Link
- Introduction to Machine Learning with Python Link
- A course in Machine Learning Link
Academic Integrity
Any plagiarism, copying, allowing copying, unpermitted aid will lead to 'zero' in the assignment/exam/project.
Past Courses