Machine Learning — Lecture Notes and Course Materials
This page provides open educational resources for the study of Machine Learning, including lecture notes, course material, examples, homework, exercises, and supporting references.
The material is intended for undergraduate and postgraduate students, teachers, researchers, and independent learners interested in machine learning and its applications in Computer Science and Artificial Intelligence.
Course Information
Course: Machine Learning (ME – Master of Engineering in Computer Science and Engineering, I Year, 2024)
Institute: MBM University, Jodhpur
Prerequisites
- Mathematics
- Statistics and Probability Theory
- Computer Fundamentals
- Python programming
- Artificial Intelligence
Syllabus
Machine Learning Course Syllabus
Course Description
Machine Learning is a major area of Artificial Intelligence concerned with developing computational methods that allow systems to learn from data and improve their performance through experience.
The course introduces fundamental machine learning paradigms, including supervised learning, unsupervised learning, reinforcement learning, neural-network-based learning, Bayesian learning, classification, regression, clustering, and association rule mining.
Learning Outcomes
- Understand the fundamental concepts and principles of machine learning.
- Distinguish between supervised, unsupervised, and reinforcement learning.
- Understand training sets, test sets, classifiers, and model evaluation.
- Apply Bayesian learning and Naive Bayes classification.
- Understand linear classifiers, perceptrons, and linear regression.
- Understand Support Vector Machines and decision trees.
- Apply clustering techniques including K-Means clustering.
- Understand association rule mining and its applications.
- Understand basic neural-network-based and deep-learning approaches.
Lecture Notes and Course Modules
Module 1: Basic Concepts of Machine Learning
Topics Covered
- Introduction to Machine Learning
- Learning in animals and machines
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Neural-network-based learning
- Deep learning networks
- Artificial neurons and neural networks
- Naive Bayes classifier
- K-Nearest Neighbour (KNN)
Learning Resources
- Slide 1: Introduction to Machine Learning — Introduction to ML, definition of machine learning, applications such as spam filtering, motivation for machine learning, and limitations of hand-coded systems.
- Slide 2: Supervised and Unsupervised Learning — Formal definition of supervised learning, unsupervised learning, and examples of unsupervised algorithms.
- Slide 3: Reinforcement Learning, Neural Network Learning and Deep Learning — Data for supervised and unsupervised learning, reinforcement learning, neural-network-based learning, and deep learning networks.
- Homework 1: Basic Concepts
- Slide 4: Reinforcement Learning and Neural Networks — Reinforcement learning, policy, reward function, state space, task examples, neural-network architecture, properties of neurons, and mathematical models.
- Slide 5: Basic Algorithms — Naive Bayes and KNN — Spam classification, vector spaces, Naive Bayes, nearest-neighbour classification, and Euclidean distance.
Module 2: Training and Test Sets and Bayesian Learning
Topics Covered
- Training and test sets
- Bayesian learning
- Bayes classifier
- Discrete attributes
- Continuous attributes
- Conditional probabilities
- Naive Bayes classification
Learning Resources
- Slide 6: Training Sets and Classifiers — Bayes theorem, training sets, classifiers, single-attribute classification, and conditional probabilities.
- Slide 7: Naive Bayes Classifiers — Discrete attribute vectors, probability computation, training sets, and Naive Bayes classification.
- Homework 2: Bayesian Learning
- Slide 8: Bayesian Classifier for Continuous Attributes — Continuous attribute vectors, Bayes formula, and estimation of parameter values.
Module 3: Linear Classifiers, Regression and Decision Trees
Topics Covered
- Linear classifiers
- Learning from examples
- Perceptron learning
- Linear regression
- Cost and loss functions
- Overfitting and underfitting
- Support Vector Machines
- Decision trees
- Classification using decision trees
Learning Resources
- Slide 9: Linear Classifier — Linear classifiers, learning from examples, and perceptron learning.
- Slide 10: Linear Regression — Linear regression, overfitting, and underfitting.
- Slide 11: Support Vector Machine — Support Vector Machines and SVM as a linear classifier.
- Homework 3: Linear Classifiers
- Slide 12: Decision Trees — Decision tree concepts, construction algorithms, and decision trees as classifiers.
Module 4: Clustering and Association Rule Mining
Topics Covered
- Data clustering and cluster analysis
- Nearest-neighbour clustering
- K-Means clustering
- Squared-error-based clustering
- Association rule mining
- Market basket analysis
- Itemsets
- Support, confidence, and lift
Learning Resources
- Slide 13: Clustering — Data clustering and cluster analysis.
- Slide 14: Clustering Algorithms — Nearest-neighbour clustering, squared error, and K-Means clustering algorithms.
- Practice Exercises — Decision trees, clustering algorithms, and association rule mining.
- Slide 15: Association Rule Mining — Market basket analysis, itemsets, confidence, support, lift, and rule mining.
About These Lecture Notes
These lecture notes are based on material developed and used while teaching Machine Learning and related areas of Computer Science. They have been organized and made available as an open educational resource for students, teachers, researchers, and independent learners.
The material is intended to complement classroom instruction and standard textbooks. Learners are encouraged to consult additional scholarly references for a deeper understanding of machine learning algorithms, mathematical foundations, and practical applications.
Related Computer Science Resources
Machine Learning is closely connected with Artificial Intelligence, Statistics, Probability, Algorithms, and other areas of Computer Science. Related learning resources available on this website include:
Further Reading
Students are encouraged to consult standard textbooks and scholarly references covering machine learning theory, statistical learning, classification, regression, neural networks, clustering, and artificial intelligence.