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MACHINE LEARNING (CST-030)

This course offers the fundamentals of machine learning and its practical applications.

Instructor: Anushka Joshi

Class Area: Haridwar University

Email: anushkazinc@gmail.com

Click on the Underlined below to access PDFs and Python files

ANNOUNCEMENTS

August 22, 2024: Classes will begin

MTE 2: 

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SYLLABUS (Click on the text to view syllabus)

EVALUATION COMPONENTS

  • Term tests/quizzes (10%)

  • Assignments + Practical (15%)

  • Class participation (5%)

  • Mid-Term Examination (30%)

  • End Term Examination (40%)

COURSE OBJECTIVES, LEARNING OUTCOMES AND PREREQUISITES

  • Understand the need for machine learning for various problem solving.

  • Study the various supervised, semi-supervised and unsupervised learning algorithms in machine learning.

  • Learn and design the appropriate machine learning algorithms for problem solving.

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Prerequisites: NIL

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LECTURE NOTES

  1. Unit 1 (Find S-Algorithm): Link​

  2. Unit 1 (Working with CSV): Link​
  3. Unit 1 (Candidate Elimination): Link
  4. Unit 1 (Linear Discrimination Analysis): Link
  5. Unit 2 (Decision Tree): Link
  6. Unit 2 (ANN): Link
  7. Unit 2 (ANN Contd...): Link
  8. Unit 3 (K-Means Algorithm): Link
  9. Unit 3 (EM Algorithm): Link
  10. Locally Weighted regression: Link

PRACTICAL WORK

  1. Find S-Algorithm Example 1: Link

  2. Working with CSV: CSV File, Link​
  3. Candidate Elimination: Link
  4. Linear Discriminant Analysis: Link
  5. Linear and Polynomial Regression: CSV File
  6. K-Means Clustering: CSV File 1
  7. K-Means Clustering: CSV File 2
  8. Heart Disease Data: CSV File

RECOMMENDED STUDY MATERIAL

​The following will be used as a reference/textbook for this course:

  1. Fei Fei Li et al., "CS231n: Deep Learning for Computer Vision", stanford

  2. Ian Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", MIT Press, 2016

  3. Christopher M. Bishop, "Pattern Recognition and Machine Learning", 2006

  4. Tom Mitchell, "Machine Learning", 1997

  5. Alpaydin, "Ethem Introduction to Machine Learning", 2014

  6. Ian Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", 2016

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