Course Title : Neural Networks with Tensorflow
Instructor : Sanasam Ranbir Singh
Teaching Assistant: Thiyam Jennil
Week 1: Introduction to Machine Learning
Reference Books:
- Machine Learning by Tom M. Mitchell click)
- Introduction to Machine Learning by Alex Smola and S.V.N. Vishwanathan click)
Lessons
- Lesson 1: Course Introduction ( PPT)
- Lesson 2: Introduction to Machine Learning
- What is Machine Learning?( PPT)
- My First Machine Learning Model??( PPT)
- Lesson 3: Different Classifier Methods
- Bayesian and Naive Bayes Classifiers( PPT)
- k-nearest neighbor and Centroid based classifier Classifier ( PPT)
- Decision Tree( PPT)
- 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
- Support Vector Machine ( PPT)
Sample Programs
- Python Tutorial - Part I (code in .ipynb format)
- Python Tutorial - Part II (code in .ipynb format)
- Building Classifiers using Scikit-learn (code in .ipynb format)
- Image Classification (code)
External Reading Resources
- What is machine learning? (click)
- Numpy Installation (click)
- Numpy Manual (click)
- Basic operations on Numpy arrays (click)
- Installation of Anaconda on a Windows system:(click)
- Installation of Anaconda on a Linux system:(click)
- Scikit learn installation:(click)
- Various supervised learning models provided by the Scikit learn (click)