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MACHINE LEARNING FOR
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BE05000081
Code: BE05000081
Credits: 3
Semester 5
EIE
Machine Learning for Industrial Automation
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Follow on Instagram for UpdatesMachine Learning for Industrial Automation Syllabus — Unit-wise Content
The official GTU curriculum for Machine Learning for Industrial Automation (subject code BE05000081) as prescribed for EIE · Semester 5 divides the course into 11 units, covering 128 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (BE05000081)Unit 1 Introduction to Machine Learning
- What is Human Learning?
- Types of Human Learning
- What is Machine Learning?
- Types of Machine Learning
- Need for Machine Learning
- Application of Machine Learning
- Languages and Tools in Machine Learning
- Issues in Machine Learning
- Problems not to be solved using Machine Learning
Unit 2 Basic of Matrix Operations, Probability and Bayesian Concept Learning
- Matrix Operations: Addition
- transpose, and inverse
- Types of Matrices Probability: Importance of Statistical tools in Machine Learning
- Concept of Probability - Frequentist and Bayesian Interpretation
- Random Variable - Discrete and Continuous
- Common Discrete Distributions - Bernoulli
- Multinomial and Multinoulli and Poisson’s distribution
- Common Continuous Distributions - Uniform
- Gaussian (Normal) and Laplace Distribution
- Multiple Random Variables - Bivariate random variables
- Joint distribution functions
- joint probability mass functions
- joint probability density functions
- conditional distributions and covariance and correlation
- central limit theorem
- Sampling distributions - Sampling with and without replacement
- mean and variance of sample
- hypothesis Testing and Monte Carlo Approximation Bayesian Concept Learning: Importance
- Bayes’ theorem - Prior
- Posterior and Likelihood
- Bayes’ Theorem and Concept Learning - Brute- force Bayesian algorithm
- concept of consistent learners
- bayes optimal classifier
- applications of naive bayes classifier
- handling continuous numeric features in naive bayes classifier
- Bayesian belief Network - Independence and conditional independence
- its use in machine learning
Unit 3 Preparing to Model
- Machine Learning Activities
- Basic types of data in Machine Learning
- Exploring structure of Data - Exploring numerical data
- plotting and exploring numerical data
- exploring categorical data
- exploring relationship between variables
- Data Quality and Remediation
- Data Pre- Processing - Dimensionality Reduction and Feature Subset Selection
Unit 4 Modelling and Evaluation
- Selecting a Model - Predictive or descriptive
- Training a model (for Program Name: Bachelor of Engineering Level: UG Branch: Instrumentation and Control Subject Code : BE05000081 Subject Name : Machine Learning for Industrial Automation supervised learning) - Holdout Method
- k-fold cross validation method
- bootstrap sampling
- lazy vs eager learner
- Model Representation and Interpretability - underfitting
- overfitting and bias variance tradeoff
- Evaluating Performance of a model - Supervised learning (Classification and Regression) and Unsupervised learning (Clustering)
- Improving performance of a model
Unit 5 Basics of Feature Engineering
- What is a feature and feature engineering?
- Feature Transformation - Construction and Extraction
- Feature Subset Selection - Issues in high dimensional data
- key drivers of feature selection - feature relevance and redundancy
- measures of feature relevance and redundancy
- Overall feature selection process
- feature selection approaches
Unit 6 Supervised Learning Regression
- Examples of Regression
- Common Regression Algorithms Simple Linear Regression
- Multiple Linear Regression
- Assumptions in Regression Analysis
- Problems in Regression Analysis
- Improving accuracy of Linear Regression Model
- Logically weighted Linear Regression
- Polynomial Regression Model
- Logistic Regression
- Multiclass Logistic Regression
- Maximum Likelihood Estimation
- Classification: Introduction and example of supervised learning
- classification model
- classification learning steps
- Common classification algorithms - k-Nearest Neighbour(kNN)
- Decision Tree
- Random Forest Model
- Support Vector Machine
Unit 7 Unsupervised Learning
- Application of unsupervised learning
- Clustering - Clustering as a machine learning task
- different types of clustering techniques
- partitioning methods
- K-Mediods: a representative object based technique
- Hierarchical Clustering
- Density based methods - DBSCAN
- Finding pattern using association rule - Definition of common terms
- Association rule
- Apriori algorithm for association rule learning
- build the apriori principle rules
Unit 8 Reinforcement Learning
- What is Reinforcement Learning?
- Elements of Reinforcement Learning - Agent
- Value function
- Introduction to Farama Foundations “The Frozen Lake RL Environment
- Reinforced Learning Algorithm - Dynamic Programming
- Monte Carlo Methods
- Temporal Difference Learning (TD)
- Sample Python Implementation of Q-Learning
- Applications of RL
Unit 9 Basics of Neural Network
- Understanding the Biological Neuron
- Exploring Artificial Neuron
- Types Program Name: Bachelor of Engineering Level: UG Branch: Instrumentation and Control Subject Code : BE05000081 Subject Name : Machine Learning for Industrial Automation of Activation Functions - Identity
- Sigmoid and Hyperbolic Tangent functions
- Architectures of Neural Networks - Single layer feed forward network
- Multilayer feed forward layer
- Competitive Network and Recurrent Network
- Learning process in ANN - Number of layers
- Direction of Signal Flow
- Number of nodes in layers
- Weight of interconnection between neurons
- Early implementations of ANN - McCulloch Pitts Model
- Rosenblatt’s perceptron
- ADALINE Network Model
- Back Propagation
- Deep Learning
Unit 10 Generative AI and LLMs
- Introduction to NLP
- Generative Models - Foundational architectures
- LLMs: Few Popular LLMs
- Challenges and Risks with LLMs
- How is ChatGPT Trained?
- Applications of Generative AI
- Prompt Engineering - Types of Prompts
- Integrating LLMS into Python using Langchain
- Pair Programming powered by Prompt Engineering
- Best Practices in Prompt Engineering
Unit 11 Case Studies and Implementations
- Detection of Iris Flower Species
- Handwritten Digit Detection Sentiment based clustering of Text Data
- Predictive Maintenance: Trends
- Challenges, and Future Directions
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