ME644

MACHINE LEARNING FOR ENGINEERS

Credits:

  

 

3-0-0-9
 

Course contents:


Mathematical preliminaries, python programming, simple/multiple linear regression, nonlinear regression, logistic regression, k-nearest neighbours, perceptrons, random forest, naïve Bayes, support vector machines, artificial neural network, clustering, dimensionality reduction

Lecturewise Breakup (Based on 50 min per lecture)


Sl. No.TopicContentsLecture
1. Introduction Various learning paradigms, definitions, examples 1
2. Programming Programming in python, libraries: scientific computing, machine learning, plotting 2
3. Mathematics for machine learning Linear algebra and vector calculus: Vector space, vector-matrix operations, norm, eigenvalue and eigenvectors, matrix decompositions, differential calculus of vectors 3
Optimization: gradient-based techniques, metaheuristic techniques, numerical implementation 3
Statistics and probability: Probability distributions, hypotheses testing, Bayes’ theorem 3
4. Supervised learning Linear/nonlinear regression, overfitting, regularization, logistic regression, naive Bayes, k-NN, decision tree, random forest, maximum likelihood, support vector machine, applications in mechanical engineering 15
5. Unsupervised learning Singular value decomposition, principal component analysis, clustering, applications in mechanical engineering 8
6. Artificial neural network Single- and multi-layer networks, activation, backpropagation, stochastic gradient descent, physics-informed neural network, applications in mechanical engineering 5
Total 40

References:

  1. Machine Learning for Engineers, R. G. McClarren, Springer

  2. A First Course in Machine Learning, S. Rogera, M. Girolami, CRC Press

  3. Machine Learning, Z-H. Zhou, Springer

  4. An Introduction to Statistical Learning, G. James et al., Springer

  5. Data-Driven Science and Engineering, S. L. Brunton, J. L. Kutz, Cambridge Uni. press

  6. Probabilistic Machine Learning for Civil Engineers, J-A. Goulet, MIT Press

  7. Machine Learning Refined, 2nd ed., J. Watt et al., Cambridge University press

  8. Machine Learning, A. Lindholm et al., Cambridge University press
 

Additive Manufacturing and Solidification

The main focus of the lab is on solid-liquid phase change (melting/solidification) involving theoretical work, multiscale computational heat transfer and fluid flow modelling (CFD), microstructure modelling, stress modelling, and experiments. The various research activities are in the area of metal additive manufacturing, welding, casting, coating and thermal energy storage.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The comprehensive approach of our group, that includes development and implementation of benchmarks, validations with controlled laboratory and actual industrial-scale quantitative experiments, and process qualification, has helped to acquire advanced scientific understanding, and predictive and control capability for defects and microstructure in solidification processes. The advanced robust models have been successfully applied to various melting/solidification related manufacturing processes (additive manufacturing, welding, surface coating, casting). Further, the multi-scale physical models, engineering thermal predictors and process selection guidelines have been applied to the area of thermal energy storage (waste heat and renewable energy).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Research Areas


Metal additive manufacturing — AM process, product and application development, DfAM; Heat transfer, CFD, DPM; Multiphysics, multiscale modelling of manufacturing processes (additive manufacturing, casting, welding, surface coating), Process, defects, microstructure and properties predictions; Machine learning tools for manufacturing; Droplet interaction with surfaces; Thermal storage, Waste heat recover.


Research Laboratories:

 

Solidification and Additive Manufacturing Laboratory

 

Associated Faculty

 

Dr. Arvind Kumar, PhD (IISc Bangalore)
Northern Laboratories, Manufacturing Science Lab
Department of Mechanical Engineering
IIT Kanpur
Kanpur 208016
Office : 0512-259-7484
Fax : 0512-259-7408
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Welcome Message

Welcome to the Department of Mechanical Engineering at IIT Kanpur. We started our journey in the year of 1960. Over the last six decades, we have grown our expertise and competence in the core Mechanical Engineering curriculum and research.

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Featured Faculty


Headlines

Dr. Digendranath Swain, an alumnus of Mechanical Engineering has been honored with the prestigious Rashtriya Vigyan Puraskar - Vigyan Yuva (Shanti Swarup Bhatnagar) Award for 2024 in the field of Space Science and Technology

August 2024

Prachi Awasthi has received the Best Oral Presentation Award in International Conference on Materials and Membranes for Water and Energy (ICMMWE-2024).

July 2024

Prajwal K Subudhi has won the first prize in the mini-symposium on "Undergraduate Research Presentation" at the Indian Conference on Applied Mechanics held at NIT Warangal.

July 2024

Prof. J. Ramkumar has been appointed as the Dean, Infrastructure and Planning for three years

June 2024

more news...

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  • Estimation of Interfacial Heat Flux for an Air-Water System placed inside a Circular Enclosure
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  • Development of novel low cost sustainable abrasive flow finishing media from ground tire rubber
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  • Development of a Transported FDF solver within LES framework
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  • A continuum model for anisotropic friction between graphene sheets accounting for surface and edge effects
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  • Development of a thermo-evaporative model of laser powder bed fusion for macroscale simulations
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  • Effect of Liquid Splattering on Thermal Performance of Jets and Sprays over Plain and Pillared Surfaces
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  • Study of Elevated Jet in Crossflow
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  • Proper Orthogonal Decomposition of Axis-Symmetric Isolated Synthetic Jet
    Malkeet Singh* and Arun K Saha

  • Method development for estimating the optical efficiency of a heliostat field
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  • Dynamics of turbulent mixing driven by Faraday instability in miscible fluids in a rotating environment
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  • The study of propeller slipstream in the advancing condition at low velocity
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  • Footstep Planning of Biped Robot for Walking on Uneven Deformable Terrain
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  • Push Recovery Control of a Humanoid Robot Incorporating Joint and Link Torsional Compliance
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  • Development of droplet condensation model of pure vapor on tubular surfaces
    Waquar Raza* and K Muralidhar

ME Department Presentation

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Announcements

Reporting date of all new PG students: Jul 18 - 19, 2024

Departmental Orientation of new PG Students: Jul 20, 2024, 2:30 PM onward, Venue: L16

PhD admission (Round 2): Application form available here. Last date of application: 4th July 2024. Online written test: 7th July, 2024, Online/Offline Interviews: 10th July, 2024

M.Tech. Admission (through COAP): Cutoff score, Vacancy and fee paid data

Selected candidates for provisional admission in PhD programme

Selected and WL candidates for MS(R) admission (Institute-sponsored category) Round 1

Rolling advertisement for faculty recruitment

 

ME Seminar

4
Nov 2022
Prof D.K. Pratihar (IIT Kharagpur)

"AI-assisted Robotic Soldier."

Time: 5:15 PM (IST), Venue: Zoom online

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ME Fast Facts