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, 15th July	Lecture 1: Meet and Greet, Introduction to Machine Learning [Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Tue, 16th July	Lecture 2: Introduction to ML, Applications of ML, Task, Performance, Experience [Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Thu, 18th July	Lecture 3: ML paradigms and applications[Slides]	Ch 1 - Mitchell
Mon, 22nd July	Lecture 4: Nearest Neighbors Introduction, Intution, One NN[Slides]	Ch 1, 8 - Alpaydin
Tue, 23rd July	Lecture 5: KNN Classification, Regression [Slides]	Ch 1, 8 - Alpaydin
Thu, 25th July	Lecture 6: KNN Numericals, Regression, type of regression Introduction [Slides]	Ch 1, 8 - Alpaydin
Mon, 29th July	Lecture 7: Simple Linear Regression, Hypothesis, Cost function, Objective function [Slides]	Ch 3 - ISLR Ch 4 - Alpaydin
Tue, 30th July	Lecture 8: Gradient, Closed form solution, Gradient discent [Slides]	Ch 3 - ISLR Ch 4 - Alpaydin
Thu, 1st Aug	Lecture 9: Multiple Linear regression [Slides][Normal solution - Solved Example] [Gradient Descent - Solved Example]	Ch 3 - ISLR Ch 4 - Alpaydin
Mon, 5th Aug	Lecture 10: Weighted KNN, Version space, Candidate Elimination introduction [Slides]
Tue, 6th Aug	Lecture 11: Candidate Elimination [Slides]	Ch 2 - Mitchell
Thu, 8th Aug	Lecture 12: Perceptron, PLA [Slides]	Ch 2 - Sebastian
Mon, 12th Aug	Lecture 13: Perceptron, Numericals [Slides]	Ch 2 - Sebastian
Tue, 13th Aug	Lecture 14: Logistic Regression [Slides]	Ch 4 - ISLR Ch 4 - Alpaydin
Mon, 19th Aug	Lecture 15: Decision Tree Introduction, Training with Classification Error as attripute selection criteria [Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Tue, 20th Aug	Lecture 16: Entropy, ID3 Introduction [Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Thu, 22nd Aug	Lecture 17: ID3, Practice problems[Practice problems]	Ch 8 - ISLR Ch 9 - Alpaydin
Mon, 2nd Sept	Lecture 18: CART, Binary tree training with Gini[Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Tue, 3rd Sept	Lecture 19: Regression with DT[Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Thu, 5th Sept	Lecture 20: Handling Continuous attributes[Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Mon, 9th Sept	Lecture 21: Probabilistic Learning, Bayes theorem, Naive Bayes classification[Slides]	Ch 6 - Mitchel
Tue, 10th Sept	Lecture 22: Naive Bayes classification example, Zero probability, Laplacian smoothing, Gaussian NB [Slides]	Ch 6 - Mitchel
Thu, 12th Sept	Lecture 23: Multinomial NB, Bernoulli NB, Multi Layer Perceptron[Slides]	Ch 6 - Mitchel
Mon, 16th Sept	Lecture 24: Multi Layer Perceptron[Slides]	Ch 11 - Alpaydin
Mon, 23th Sept	Lecture 25: Multi Layer Perceptron[Slides]	Ch 11 - Alpaydin
Tue, 24th Sept	Lecture 26: Introduction to Unsupervised learning, Clustering - Introduction, K-Means (Partitioning based) clustering[Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Thu, 26th Sept	Lecture 27: K-Means clustering, K-Means++ initialization [Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Mon, 30th Sept	Lecture 28: K-Modes Clustering, Introduction to hierarchical Clustering, AGNES [k_modes Slides] [AGNES Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Tue, 1st Oct	Lecture 29: Hierarchical Clustering, DIANA[Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Thu, 3rd Oct	Lecture 30: Desity based Clustering, DBSCAN, Self Organizing Maps(SOM) [DBSCAN Slides] [SOM Slides]
Mon, 7th Oct	Lecture 31: Guest Lecture
Tue, 8th Oct	Lecture 32: Class Imbalancing, Techniques for handling imbalanced data, Classification metrics[Slides]	Ch 5 - ISLR
Mon, 21 Oct	Lecture 33: Maxumum margin classifier[Slides]	Ch 13 - Alpadin Ch 9 - ISLR
Tue, 22 Oct	Lecture 34: Soft margin classifier (SVC)[Slides]	Ch 13 - Alpadin Ch 9 - ISLR
Thu, 24 Oct	Lecture 35: Kernel SVM[Slides]	Ch 13 - Alpadin Ch 9 - ISLR
Mon, 4th Nov	Lecture 36: SVM - Numericals [Slides]	Ch 9 - ISLR Ch 13 - Alpaydin
Tue, 5th Nov	Lecture 37: Expectation Maximization[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Thu, 7th Nov	Lecture 38: Ensemble Models - Introduction[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Mon,11th Nov	Lecture 39: Boosing, Random Forest[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Tue, 12th Nov	Lecture 40: Bagging, Adaboost, Stacking[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Mon, 14th Nov	Lecture 41: Reinforcement Learning - Introduction[Slides]	Ch 13 - Tom Mitchell Ch 18 - Alpaydin
Thu, 14th Nov	Lecture 42: Reinforcement Learning[Slides]	Ch 13 - Tom Mitchell Ch 18 - Alpaydin
Mon, 18th Nov	Lecture 43: Q- Learning[Slides]	Ch 13 - Tom Mitchell Ch 18 - Alpaydin
Tue, 19th Nov	Lecture 44: CAT 2 QP DIscussion
Thu, 21st Nov	Lecture 45: Discussion
Course Completed. All the Best!