CSE4020 - Machine Learning
- Instructor
- Dr. Bhargavi R
- Venue
| Slot D1 - | Delta Block 305 |
| Slot D2 - | Delta Block 305 |
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  |
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| Thu, 08th Dec | Lecture 1: Intro [Slides] |
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| Mon, 12th Dec | Lecture 2: Intro [Slides] |
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| Mon, 19th Dec | Lecture 3: ML Paradigms [Slides] |
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| Thu, 22 Dec | Lecture 4: Supervised Learning, Introduction to Nearest Neighbors [Slides] |
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| Welcome to 2023. Happy New Year! | |||
| Thu, 5 Jan | Lecture 5: Nearest Neighbors Classification [Slides] |
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| Thu, 9th Jan | Lecture 6: Nearest Neighbors Regression, Perceptron [Slides] |
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| Thu, 12th Jan | Lecture 7: Perceptron, Adaline [Slides] [Perceptron Example] |
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Assignment 1 Release |
| Fri, 13th Jan | Lecture 8: Regression, Linear Regression [Slides] |
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| Fri, 19th Jan | Lecture 9: Regression, Linear Regression [Slides], [Example problem] |
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| Mon, 30th Jan | Lecture 10: Logistic Regression [Slides] |
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| Thu, 02nd Feb | Lecture 11: Logistic Regression, Decision Tree Introduction [Slides] |
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| Sat, 04th Feb | Lecture 12: Decision Tree - ID4 [Slides] |
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| Mon, 06th Feb | Lecture 13: Decision Tree - CART [Slides] |
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| Thu, 09th Feb | Lecture 14 : Decision Tree - Regression, Handling Continuous valued attributes [Slides] |
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| Mon, 13th Feb | Lecture 15 : Multi Layer Perceptron(MLP), ANN [Slides] |
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| Thu, 16th Feb | Lecture 16 : Introduction to Unsupervised learning, Introduction to Kmeans [Slides] |
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| Mon, 20th Feb | Lecture 17 : Kmeans, Kmeans++ [Slides] |
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| Thu, 23rd Feb | Lecture 18 : Kmeans++, kModes [Slides] |
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| Sat, 25th Feb | Lecture 19 : Probabilistic learning, Bayes Theorem, Naive Bayes [Slides] |
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| Mon, 27th Feb | Lecture 20 : Multinomial NB, Bernoulli NB for Text classification [Slides] |
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| Mon, 6th Mar | Lecture 21 : Hierarchical Clustering, AGNES [Slides] |
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| Thu, 9th Mar | Lecture 22 : DIANA [Slides] |
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| Mon, 20th Mar | Lecture 23 : SVM [Slides] | ||
| Thu, 23rd Mar | Lecture 24 : SVM [Slides] |
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| Mon, 27th Mar | Lecture 25 : Model Evaluation [Slides] |
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| Thu, 30th Mar | Lecture 26 : Ensemble Models [Slides] |
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| Mon, 3rd April | Lecture 27 : Ensemble models, Resampling[Slides] |
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| Thu, 6th April | Lecture 28 : Resampling, Practical issues - Imbalanced Data issues and Techniques to Overcome, Performance metrics [Slides] |
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| Mon, 10th April | Lecture 29 : Principal Component Analysis [Slides] |
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| Thu, 13th April | Lecture 30 : PCA, Course look back [Slides] |
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| Course Completed. All the Best! | |||