Machine Learning for Data Science

Machine Learning
for Data Science

A mathematically and conceptually intense course with rewarding coding modules.

View Evaluation Plan See TA Tutorials

Aims and Objectives

  1. To understand the machine learning concepts and choose appropriate techniques/methods for various tasks.
  2. To be able to build a data analytics pipeline suited for various real-world applications with varying data dimensions e.g., 1D signal processing (e.g., audio/speech/sensory), 2D/3D signal processing (images, business/commercial survey data analysis, scan volumes etc).
  3. Evaluate the performance of the method with respect to a gold standard target and analyze the competency of the method.
  4. Determine the significance in the improvement/change in the performance of the method given the population/dataset size in the real-world scenarios.

The Team

Instructor

Vaanathi Sundaresan

CDS

vaanathi@iisc.ac.in

Teaching Assistants

Course Details

  • Teams code: t11c3j7
  • Seats: Limited to 80 students (FCFS)
Prerequisite: Basic knowledge in linear algebra, probability and a good proficiency in programming (Python) or instructor consent.

Highlights

  1. Theoretical and practical concepts of ML in parallel
  2. Slides + notes + discussion of exam key
  3. Project‑based assignments for easier understanding
  4. Open days for clarifications
  5. Strong foundation of ML for advanced courses