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!
Thu, 12th Dec	Lecture 1: Meet and Greet, Introduction to Machine Learning [Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Tue, 16th Dec	Lecture 2: Introduction to ML, Applications of ML, Formal definition of ML [Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Wed, 18th Dec	Lecture 3: ML paradigms and applications - Data perspective and Model perspective [Slides]	Ch 1 - Mitchell
Thu, 19th Dec	Lecture 4: Nearest Neighbors Introduction, Intution, One NN [Slides]	Ch 1, 8 - Alpaydin
Thu, 2nd Jan 2025	Lecture 5:One NN, KNN Classification [Slides]	Ch 1, 8 - Alpaydin
Sat, 4th Jan	Lecture 6: KNN Numericals, NN basedRegression [Slides]	Ch 1, 8 - Alpaydin
Mon, 6th Jan	Lecture 7: Regression, type of regression Introduction, Simple Linear Regression, Hypothesis, Cost function, Objective function [Slides]	Ch 3 - ISLR Ch 4 - Alpaydin
Wed, 8th Jan	Lecture 8: Gradient, Closed form solution, Gradient discent [Slides][Normal solution - Solved Example] [Gradient Descent - Solved Example]	Ch 3 - ISLR Ch 4 - Alpaydin
Thu, 9th Jan	Lecture 9: Multiple Linear regression [Slides]	Ch 3 - ISLR Ch 4 - Alpaydin
Mon, 13th Jan	Lecture 10: Multi collinearity, Logistic Regression [Slides]	Ch 4 - ISLR Ch 4 - Alpaydin
Mon, 20th Jan	Lecture 11: Logistic regression numericals, Perceptron, PLA [Slides]	Ch 2 - Sebastian
Wed, 22nd Jan	Lecture 12: Perceptron, Numericals [Slides]	Ch 2 - Sebastian
Thu, 23rd Jan	Lecture 13: Multi Layer Perceptron[Slides]	Ch 11 - Alpaydin
Mon, 3rd Feb	Lecture 14: Multi Layer Perceptron[Slides]	Ch 11 - Alpaydin
Wed, 5th Feb	Lecture 15: Decision Tree Introduction, Training with Classification Error as attripute selection criteria [Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Thu, 6th Feb	Lecture 16: Entropy, ID3 Introduction, C4.5 [Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Sat, 8th Feb	Lecture 17: CART, Binary tree training with Gini, Handling Continuus attributes[Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Mon, 10th Feb	Lecture 18: Probabilistic Learning, Bayes theorem, Naive Bayes classification[Slides]	Ch 6 - Mitchel
Wed, 12th Feb	Lecture 19: Naive Bayes classification example, Zero probability, Laplacian smoothing, Gaussian NB [Slides]	Ch 6 - Mitchel
Thu, 13th Feb	Lecture 20: Maxumum margin classifier, Soft margin classifier[Slides]	Ch 13 - Alpadin Ch 9 - ISLR
Mon, 17 Feb	Lecture 21: Kernel Trick, SVM, Numericals[Slides]	Ch 13 - Alpadin Ch 9 - ISLR
Wed, 19th Feb	Lecture 22: Introduction to Unsupervised learning, Clustering - Introduction, K-Means (Partitioning based) clustering[Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Thu, 20th Feb	Lecture 23: K-Means clustering, K-Means++ initialization [Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Mon, 24th Feb	Lecture 24: Guest Lecture
Mon, 3rd March	Lecture 25: K-Modes Clustering, Introduction to hierarchical Clustering, AGNES [k_modes Slides] [AGNES Slides]
Wed, 5th March	Lecture 26: Hierarchical Clustering, DIANA[Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Thu, 6th March	Lecture 27: Desity based Clustering, DBSCAN, GMM [DBSCAN Slides] [GMM Slides]
Mon, 10th March	Lecture 28: Candidate elimination algorithm, Self Organizing Maps(SOM) [SOM Slides] Candidate Elimination]
WED, 12TH March	Lecture 29: Classification metrics[Slides]	Ch 5 - ISLR
Thu, 13TH March	Lecture 30: Discussion
Mon, 24th March	Lecture 31: Class Imbalancing, Techniques for handling imbalanced data[Slides]	Ch 5 - ISLR
Wed, 26th March	Lecture 32: Ensemble Models - Introduction[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Thu,27th March	Lecture 33: Bagging, Random Forest[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Wed, 2nd April	Lecture 34: Boosting, Adaboost[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Thu, 3rd April	Lecture 35:Stacking, t-SNE[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Mon, 7th April	Lecture 36:PCA[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Mon, 7th April	Lecture 37:PCA[Slides]	Ch 10 - ISLR
Wed, 9th April	Lecture 38: Reinforcement Learning - Introduction[Slides]	Ch 13 - Tom Mitchell Ch 18 - Alpaydin
Thu, 10th April	Lecture 39: Reinforcement Learning[Slides]	Ch 13 - Tom Mitchell Ch 18 - Alpaydin
Wed, 16th April	Lecture 40: Q- Learning[Slides]	Ch 13 - Tom Mitchell Ch 18 - Alpaydin
Course Completed. All the Best!