Machine Learning

Pre-requisites, Topics and outcomes

Course Name: ME (Master of Engineering (CSE)), I year, 2024. Institute name: MBM University, Jodhpur

Pre-requisites: Mathematics, Statistics, Probability Theory, Computer Fundamentals, Python, AI

Syllabus

Basic Concepts

Basics, learning in animals, supervised learning, unsupervised learning, Reinforcement learning, Neural Network based learning, Deep learning Networks, neural networks, components of a neuron, ANN, Naive Bayes, KNN, classifiers

Slide 1: Introduction Introduction to ML, Machine Learning defined, example of application in spam filtering, Problem with baits shyness learning, criteria about theory of ML, justification for machine learning, why hand coded system are not enough?
Slide 2: Supervised and Unsupervised Learning Supervised Learning, Its formal definition, unsupervised learning, unsupervised algorithm examples
Slide 3: Reinforcement learning, neural network based learning, Deep Learning Data for supervised and unsupervised Learning, Reinforcement learning, learning through neural networks, Deep learning Networks
HW #1: Basic Concepts
Slide 4: RL and NN Reinforcement learning: policy, reward function, mapping, state-space, task example, function in RL. Simple architecture of NN, properties of neuron, its applications. Basic model of artificial neuron, its connections, and math. expression.
Slide 5: Basic Algorithms: Naive Bayes & KNN Labeling email as spam/nospam, Vector space, applying Naive Bayes, Nearest neighbor classifier, Euclidean distance.

Training and test sets, Bayesian Learning

Training and test sets, Bayesian Learning, Bayes Classifier for discrete and continuous attributes.

Linear Classifier and Linear Regression

Linear classifier, linear regression, cost/loss function, overfitting, underfitting, SVM, SVM as linear classifier ...