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!
Wed, 9th July	Lecture 1: Meet and Greet, Introduction to Machine Learning [Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Fri, 11th July	Lecture 2: Introduction to ML, Applications of ML, Formal definition of ML [Slides]	Ch 1 - Alpaydin Ch 1 - Mitchell
Mon, 14th July	Lecture 3: ML paradigms and applications - Data perspective and Model perspective [Slides]	Ch 1 - Mitchell
Wed, 16th July	Lecture 4: ML paradigms and applications - Data perspective and Model perspective [Slides]	Ch 1, 8 - Alpaydin
Fri, 18th July 2025	Lecture 5:Nearest Neighbors Introduction, Intution One NN, KNN Classification [Slides]	Ch 1, 8 - Alpaydin
Mon, 21 July	Lecture 6: KNN Numericals, NN basedRegression, Advantages and limitations of NN [Slides]	Ch 1, 8 - Alpaydin
Wed, 23rd July	Lecture 7: Regression, type of regression Introduction, Simple Linear Regression, Hypothesis, Cost function, Objective function [Slides]	Ch 3 - ISLR Ch 4 - Alpaydin
Fri, 25th July	Lecture 8: Gradient, Closed form solution, Gradient discent [Slides][Normal solution - Solved Example] [Gradient Descent - Solved Example]	Ch 3 - ISLR Ch 4 - Alpaydin
Mon, 28th July	Lecture 9: Multiple Linear regression [Slides]	Ch 3 - ISLR Ch 4 - Alpaydin
Wed, 30th July	Lecture 10: Multi collinearity, Logistic Regression [Slides]	Ch 4 - ISLR Ch 4 - Alpaydin
Fri, 1st Aug	Lecture 11: Logistic regression, numericals [Slides]	Ch 2 - Sebastian
Mon, 4th Aug	Lecture 12: Perceptron Introduction, PLA [Slides]	Ch 2 - Sebastian
Wed, 6th Aug	Lecture 13: Perceptron, Numerical [Slides]	Ch 2 - Sebastian
Sat, 9th Aug	Lecture 14: Probabilistic Learning, Bayes theorem, Naive Bayes classification[Slides]	Ch 6 - Mitchel
Mon, 11th Aug	Lecture 15: Naive Bayes classification example, Zero probability, Laplacian smoothing, Gaussian NB [Slides]	Ch 6 - Mitchel
Mon, 11th Aug	Lecture 16: Multinomial NB, Bernoulli NB, Introduction to MLP [Slides]	Ch 6 - Mitchel
WED, 13TH Aug	Lecture 17: Classification metrics[Slides]	Ch 5 - ISLR
Mon, 25th Aug	Lecture 18: Multi Layer Perceptron[Slides]	Ch 11 - Alpaydin
Fri, 29th Aug	Lecture 19: Decision Tree Introduction, Training with Classification Error as attripute selection criteria [Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Mon, 1st Sept	Lecture 20: Entropy, ID3 Introduction, C4.5 [Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Wed, 3rd Sept	Lecture 21: CART, Binary tree training with Gini, Handling Continuus attributes, Tree Pruning[Slides]	Ch 8 - ISLR Ch 9 - Alpaydin
Fri, 8th Sept	Lecture 22: Maxumum margin classifier, Soft margin classifier[Slides]	Ch 13 - Alpadin Ch 9 - ISLR
Wed, 10th Sept	Lecture 23: Kernel Trick, SVM, Numericals[Slides]	Ch 13 - Alpadin Ch 9 - ISLR
Fri, 12th Sept	Lecture 24: Candidate elimination algorithm [Candidate Elimination]
Mon, 15th Sept	Lecture 25: Introduction to Unsupervised learning, Clustering - Introduction, K-Means (Partitioning based) clustering[Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Wed, 17th Sept	Lecture 26: K-Means clustering, K-Means++ initialization [Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Fri, 19th Sept	Lecture 27: K-Modes Clustering, Introduction to hierarchical Clustering, AGNES [k_modes Slides] [AGNES Slides]
Mon, 22nd Sept	Lecture 28: Hierarchical Clustering, DIANA, Self Organizing Maops (SOM)[Slides] [SOM Slides]	Ch 7 - Alpaydin Ch 10 - ISLR
Wed, 24th Sept	Lecture 29: Desity based Clustering, DBSCAN [DBSCAN Slides]
Fri, 26th Sept	Lecture 30: Guest Lecture - Feature Engineering
Mon, 29th Sept	Lecture 31:Eigen values, Eigen vectos, Introduction to PCA[Slides]	Ch 10 - ISLR Ch 6 - Alpaydin
Fri, 3rd Oct	Lecture 32:PCA[Slides]	Ch 10 - ISLR Ch 6 - Alpaydin
Mon, 13th Oct	Lecture 33: Class Imbalancing, Techniques for handling imbalanced data[Slides]	Ch 5 - ISLR
Wed, 15th Oct	Lecture 34: Class Imbalancing, Techniques for handling imbalanced data[Slides]	Ch 5 - ISLR
Fri, 17th Oct	Lecture 35: Bias variance tradeoff, Ensemble Models - , Introduction to ensemble models[Model evaluation, Bias-variance Slides] [Ensemble learnings]	Ch 8 - ISLR Ch 11 - Alpaydin
Mon,27th Oct	Lecture 36: Bagging, Random Forest, Introduction to Boosting[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Wed, 29th Oct	Lecture 37: Adaboost, Stacking[Slides]	Ch 8 - ISLR Ch 11 - Alpaydin
Mon, 3rd Nov	Lecture 38: Reinforcement Learning - Introduction[Slides]	Ch 13 - Tom Mitchell Ch 18 - Alpaydin
Wed, 5th Nov	Lecture 39: Reinforcement Learning, MDP, Value function[Slides]	Ch 13 - Tom Mitchell Ch 18 - Alpaydin
Fri, 7th Nov	Lecture 40: Q- Learning, Q-learning with Temporal difference, SARSA[Slides]	Ch 13 - Tom Mitchell Ch 18 - Alpaydin
Mon, 10th Nov	Lecture 41: DIscussion
Wed, 12th Nov	Lecture 42: Discussion
Course Completed. All the Best!