18B1WCI674: Machine Learning Lab

This course will introduce fundamental concepts of machine learning

Instructors

  • Prof. Vivek Kumar Sehgal (VSG)
  • Dr. Kushal Kanwar (KLK)
  • Dr. Meghna Dhalaria (MGN) (Course Coordinator)
  • Mr. Sandeep Kumar Patel (SKP)

Syllabus

This is a 0-0-2 (L–T–P) course.

Syllabus includes: Introduction to libraries: numpy, pandas, matplotlib, seaborn, scikit-learn; Decision tree using Entropy and Information Gain; Random forest tree and evaluation; Linear Regression; Naive Bayes Classifier; Logistic Regression; Support Vector Machine Kernel function and Kernel SVM; Dimensionality reduction techniques: Subset Selection, PCA, FA, MDS, LDA.

Text Books

  1. Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997. ISBN: 0070428077.
  2. Sebastian Raschka, “Python Machine Learning”, Packt Publishing Ltd.
  3. Andreas C. Müller, Sarah Guido, “Introduction to Machine Learning with Python”, O'Reilly Media, Inc.
  4. Sunila Gollapudi, “Practical Machine Learning”, Packt Publishing Ltd.
  5. Wes McKinney, “Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython”, O'Reilly Media, Inc.
  6. Willi Richert, “Building Machine Learning Systems with Python”, Packt Publishing Ltd.

Lecture Schedule

Topic Slides
Machine Learning Lab Marking Scheme
ML Lab: Assignment-1
ML Lab: Assignment-2
ML Lab: Assignment-3
ML Lab: Assignment-4
ML Lab: Assignment-5