Machine Learning Club

Coordinator: Parth Patwa
  • Data Science intern Hike Messenger
  • Data Science intern
  • AutomationEdge
  • Total 7 months of industry work experience in
  • Machine Learning (ML) and Data Science
  • Previously conducted ML sessions through Gradient,
  • SocAIty, IOTA.
  • ML and Deep Learning Certifications from Stanford,
  • Coursera, IIT Kharagpur
Faculty advisor: Dr Snehasis Mukherjee, Dr Prerana Mukherjee
Background:
  • ML is an extremely hot topic in the industry, with all companies hiring in this field.
  • Practical experience and good knowledge is a must to succeed in Industry.
  • Data, algorithms, and computation are advancing. We need skills to leverage that.
  • Machine Learning has applications in all domains (Healthcare, finance, entertainment, surveillance, etc).
Objectives:
  • Educate members on what exactly is ML and its possibilities. (primary)
  • Developing the interests of members in ML. (primary)
  • Making members familiar with frameworks, best practices. (primary)
  • The members should understand the algorithms, its maths and Real World uses. (primary)
  • Practical and hands-on experience. (primary)
  • Inter-college participation. (secondary)
  • Making UG1 and UG2 industry ready and try to provide internships. (secondary)
Proposed Activities:
1) Short term (this sem, Through lectures, coding sessions, extempore):

a) What is ML? Where is it used?
b) Recent advances in ML, infinite possibilities.
c) Supervised vs unsupervised learning.
d) Linear, logistic regression, gradient descent, knn.
e) Practical hands-on coding, sklearn.
f) Intro to DL.

2) Long term (lectures, coding sessions, peer learning):

a) Debugging, real-world challenges.
b) Data challenges, Hyperparameter optimization.
c) Advanced DL.
d) Setting up end-to-end projects from data collection to model selection
e) Capstone project on kaggle.

3) Invited talks:

a) Invited talks by domain experts (Natural Language Processing, Computer Vision)
b) Women in ML (based on the availability of speakers)

Scope to conduct event:
  • Possibility of conducting Hackathons in data science/workshops.
  • Example chatbot, disease prediction etc.
Registrations:
  • No constraint on the number, as most will be new to this field.
  • Main focus on UG1 and UG2, but all are welcome.
  • Girls will be strongly encouraged.
Resources required:
  • Classroom with basic facilities.
Any other information:
  • The depth and workload will be increased/decreased based on response and interest.
  • Projects, certificates will be offered based on performance.
  • Optional reading/take-home assignments will be given and assessed.
  • Close Collaboration with IOTA on projects.
  • Peer learning will be highly motivated.