Course Title : Neural Networks with Tensorflow

Instructor : Sanasam Ranbir Singh
Teaching Assistant: Thiyam Jennil

Week 1: Introduction to Machine Learning

Reference Books:

  1. Machine Learning by Tom M. Mitchell click)
  2. Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan click)

Lessons

  1. Lesson 1: Course Introduction ( PPT)
  2. Lesson 2: Introduction to Machine Learning
    1. What is Machine Learning?( PPT)
    2. My First Machine Learning Model??( PPT)
  3. Lesson 3: Different Classifier Methods
    1. Bayesian and Naive Bayes Classifiers( PPT)
    2. k-nearest neighbor and Centroid based classifier Classifier ( PPT)
    3. Decision Tree( PPT)
      1. Some of the examples and figures are taken from the book Tom M. Mitchell, Machine Learning, McGraw-Hill, 1997 and slides from Allan Neymark CS157B – Spring 2007
    4. Support Vector Machine ( PPT)

Sample Programs

  1. Python Tutorial - Part I (code in .ipynb format)
  2. Python Tutorial - Part II (code in .ipynb format)
  3. Building Classifiers using Scikit-learn (code in .ipynb format)
  4. Image Classification (code)

External Reading Resources

  1. What is machine learning? (click)
  2. Numpy Installation (click)
  3. Numpy Manual (click)
  4. Basic operations on Numpy arrays (click)
  5. Installation of Anaconda on a Windows system:(click)
  6. Installation of Anaconda on a Linux system:(click)
  7. Scikit learn installation:(click)
  8. Various supervised learning models provided by the Scikit learn (click)