Shreyas-Bhat/Introduction-and-Applications-of-Machine-Learning

Introduction-and-Applications-of-Machine-Learning

Instructors: Shreyas Bhat, S I Harini, Rishav Mukherji
Mentors: Hardik Shah

Course Description and Outcome

The course intends to help students with no experience in machine learning take their first steps in the field. We will start off with the basic Machine learning theory and look into essential tools needed for ML; libraries like numpy and pandas. Then we will proceed to teach the students about various machine learning algorithms, the maths behind it. With the help of scikit learn, the students will learn how to implement various models as well.

In the fourth week we intend to introduce the students to Deep Learning and how to implement perceptrons and neural networks in Pytorch followed by a basic introduction to CV in the last week and subsequent project completion.

Course Plan

Week	Topic	Subtopic
1	Machine Learning Introduction	Basic concepts of Python, Numpy, and Pandas Introducing supervised, unsupervised learning and other pre-requisites
2	Theory of ML	Introduction to Machine Learning algorithms Bayes' rule Loss functions Gradient descent
3	Applying ML and Project Discussion	Linear regression Logistic regression SVMs Decision trees (binary classification and multi-class classification)
4	Introduction to Deep Learning	Multi-layer Perceptrons Backpropagation Data loading Data augmentation
5	Introduction to Computer Vision and Project Completion	Convolutional Neural Networks and image classification Introducing architectures for CV tasks

Timing

Tuesday - 6:00PM to 7:30PM
Thursday - 6:00PM to 7:30PM
Saturday - 3:30PM to 5:00PM