BCSE209L - Machine Learning

Course Overview

This course provides an introduction to Machine Learning and applications of Machine Learning using Scikit-Learn. The course covers the topics like: Supervised learning, Unsupervised learning, Model Evaluation and practical issues. More details on the topics covered can be obtained from the syllabus

Syllabus

You can find the syllabus of this course here

Prerequisites

Prior knowledge of the following subjects help you to understand appreciate the Machine Learning Course better

• Linear Algebra
• Probability and Statistics
• Calculus
• Python

Textbooks

Following are the text books for reference -

• Ethem Alpaydin, "Introduction to Machine Learning" , MIT Press, Prentice Hall of India, Third Edition 2014.
• Tom Mitchell, "Machine Learning", McGraw Hill, 3rd Edition,1997.
• Kevin P. Murphy ”Machine Learning: A Probabilistic Perspective”, The MIT Press, 2012.
• Gareth James , Daniela Witten , Trevor Hastie , Robert Tibshirani, "An Introduction to Statistical Learning with Applications in R"
• Sebastian Raschka, Vahid Mirjalili , "Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2" ,Third Edition

Tentative Schedule

Date	Lecture	Readings	Announcements
Welcome to Machine Learning!
Mon, 24th April	Lecture 1: Meet and Greet, Introduction to Machine Learning [Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Tue, 25th April	Lecture 2: Introduction to ML, Applications of ML [Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Thu, 27th April	Lecture 3: Task, Performance, Experience [Slides]	Ch 1 - Mitchell
Sat, 29th April	Lecture 4: ML paradigms - Supervised Learning[Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Tue, 2nd May	Lecture 5: ML paragms - Unsupervised learning, Reinforcement Learning [Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Thu, 4th May	Lecture 6: Nearest Neighbors model- Introduction, one NN [Slides]	Ch 1, 8 - Alpaydin
Sat, 6th May	Lecture 7: Nearest Neighbors model - kNN, Regression [Slides]	Ch 1, 8 - Alpaydin
Mon, 8th May	Lecture 8: Tutorial 1
Tue, 9th May	Lecture 9: Linear Regression - Introduction, Simple linear regression Hypothesis, Cost function [Slides]	Ch 3 - ISLR Ch 4 - Alpaydin
Thu, 11th May	Lecture 10: Linear Regression - Leat squares, Gradient discent, Closed form solution [Slides] [Normal solution - Solved Example] [Gradient Descent - Solved Example]	Ch 3 - ISLR Ch 4 - Alpaydin
Mon, 15th May	Lecture 11: Linear Regression - Multiple linear regression [Slides]	Ch 3 - ISLR Ch 4 - Alpaydin
Tue, 16th May	Lecture 12: Logistic Regression [Slides]	Ch 4 - ISLR Ch 4 - Alpaydin
Thu, 18th May	Lecture 13: Probabilistic learning, Bayes Theorem, Naive Bayes[Slides]	Ch 6 - Mitchell
Mon, 22nd May	Lecture 14: Naive Bayes - laplacian smoothing, Multinomial NB[Slides]	Ch 6 - Mitchell
Tue, 23rd May	Lecture 15: Bernoulli NB[Slides]	Ch 6 - Mitchell
Thu, 25th May	Lecture 16: Tutorial2
Thu, 01st June	Lecture 17: Perceptron, Adaline[Slides] [Perceptron Solved example]	Ch 2 - Sebastian
Sat, 3rd June	Lecture 18: Model Evaluation[Slides] [Crossvalidation]	Ch 5 - ISLR
Mon, 5th June	Lecture 19: MLP[Slides]	Ch 11 - Alpaydin
Tue, 6th June	Lecture 20: Numericals on Perceptron, MLP[Slides]	Ch 2 - Sebastian Ch 11 - Alpaydin
Thu, 8th June	Lecture 21: Decision Tree[Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Sat, 10th June	Lecture 22: Decision Tree[Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Mon, 12th June	Lecture 23: Decision Tree[Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Tu2, 13th June	Lecture 24: Decision Tree[Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Thu, 15th June	Lecture 25: Introduction to Unsupervised learning, Clustering - Introduction, K-Means (Partitioning based) clustering[Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Mon, 19th June	Lecture 26: K-Means clustering, K-Means++ initialization[Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Tue, 20th June	Lecture 27: K-Modes Clustering, Introduction to hierarchical Clustering[Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Thu, 22nd June	Lecture 28: Hierarchical Clustering, AGNES[Slides]	Ch 10 - ISLR
Thu, 24th June	Lecture 29: Hierarchical Clustering, DIANA[Slides]	Ch 6, Ch 10 - ISLR Ch 2 - Sebastian
Mon, 26th June	Lecture 30: Density Based Clustering, DBSCAN[Slides]
Tue, 27th June	Lecture 31: Revision
Mon, 3rd July	Lecture 32: SVM - Hard margin classifier[Slides]	Ch 9 - ISLR Ch 13 - Alpaydin/li>
Tue, 4th July	Lecture 33: SVM - Soft margin classifier, Kernel trick,SVM [Slides]	Ch 9 - ISLR Ch 13 - Alpaydin
Thu, 6th July	Lecture 34: SVM - Numericals, Ensemble Models [Slides][Solved example ]	Ch 9 - ISLR Ch 13 - Alpaydin
Mon, 10th July	Lecture 35: Ensemble Models - Bagging - Random Forest[Slides]	Ch 8 - ISLR
Tue, 11th July	Lecture 36: Ensemble Models - Boosting - AdaBoost, Stacking, Introduction to Principal Component Analysis[Slides]	Ch 8 - ISLR Ch 6, Ch 10 - ISLR Ch 5 - Sebastian
Tue, 11th July	Lecture 37: Guest Lecture
Thu, 13th July	Lecture 38: Principal Component Analysis[Slides]	Ch 6, Ch 10 - ISLR Ch 5 - Sebastian
Mon, 17th July	Lecture 39: Reinforcement Learning[Slides]	Ch 13 - Tom Mitchell
Tue, 18th July	Lecture 40: Reinforcement Learning[Slides] [Mitchell Book chapter]	Ch 13 - Tom Mitchell
Course Completed. All the Best!