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 Engineers 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. 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 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

 

Ph.D. admission (spot round)

Fresh applications are invited for Ph.D. admission for the following categories:


(i) direct Ph.D. admission (with B.E./B.Tech. as the qualifying degree)


For candidates having B.E./B.Tech. degree from a centrally funded technical institution (e.g., IITs, NITs, IIITDM, IIEST, etc.), the requirement of a GATE score is waived.


(ii) regular Ph.D. admission (with M.E./M.Tech. as the qualifying degree)


Eligibility criteria and other relevant details are available here.


Also, the relevant details on our Ph.D. curriculum are available here.


Interested candidates are required to fill the Google form: https://forms.gle/FGqaamGdbpXcWK2q7


The last date for application is 3rd July 2023.


Date of PhD admission test: 10th July, 2023.


Reporting time: 8:30 AM at Tutorial Block.


Result of PhD admission (second round) is available here.

 

ME334

Experiments in Mechanical Engineering - II

Credits:

 

 

L-0T-4P-0A (4 Credits)

 

Objectives


This course exposes the students to experiments on (a) Energy conversion (b) Mechanisms (c) Heat Transfer (d) Experimental stress analysis.

Course content


Experiments on (a) Energy conversion (b) Mechanisms (c) Heat Transfer (d) Experimental stress analysis.

Tentative list of experiments

A.

1. Evaluation of performance characteristics for a Francis Turbine

2. Evaluation of performance characteristics for a Pelton Turbine

3. Performance Study of a single cylinder four-Stroke diesel engine with variable compression ratio

4. Study of a refrigeration system with series and parallel evaporators

B.


Lab 1

Make the following mechanisms using the kit provided

1. Mechanism 1A: Watt’s mechanism

2. Mechanism 1B: The multi-bar pantograph mechanism

3. Mechanism 1C: Four bar mechanism with a translational link

4. Mechanism 1D: Peaucellier-Lipkin linkage


Lab 2

1. Mechanisms 2A

2. Need to form

3. Various inversions of a 3R-1P mechanism satisfying Grashof Criterion)

4. One 3R-1P non-Grashof mechanism

5. Mechanisms 2B

6. Construct a Grashof and a non-Grashof 4R mechanism. consider all possible cases of a Grashof linkage

7. Mechanisms 2C

8. Make a constrained 8-bar mechanism which consists of only binary and ternary links

9. Mechanisms 2D (BONUS)

10. Various inversions of R-P-R-P and 2R-2P mechanism satisfying Grashof Criterion


Lab 3

1. Verify the torque relationship as Ti+ T0 + Th= 0of epicyclic gear trains


Lab 4

1. Cams

2. Aim: Come up with the cam profile from the observed displacement diagram


Lab 5

1. Static and Dynamic Balancing of Rotor


Lab 6: Two Plain Balancing


C.

1. Unsteady Heat Conduction

2. Heat Transfer by Force Convection (Pin-fin)

3. Heat Transfer through Extended Surface

4. Critical Heat Flux (Pool boiling)

5. Heat Transfer by Natural Convection

6. Measurement of Emissivity

7. Calibration of Thermocouples


D.

1. Cantilver Beam

2. Portal Frame

3. Combined Stresses

4. Stress Analysis Using Photelasticity

5. Determination of Shear Modulusn

6. DIC on Universal Testing Machine (UTM)

Proposing instructors: DUGC, ME

 

ME333

Experiments in Mechanical Engineering - I

Credits:

 

 

0L-0T-1P-0A (3 Credits)

 

Objectives


This course exposes the students to experiments on (a) Vibration and dynamics (b) Fluid mechanics (c) Material testing

Course content


Experiments on (a) Vibration and dynamics (b) Fluid mechanics (c) Material testing

Tentative list of experiments

A.


1. Determination of the effective radius of gyration of an irregular body through torsional oscillation of a trifilar suspension

2. Measuring free and forced vibration response of a rigid beam-spring-damper system

3. Tuned vibration absorber

4. Vibration transmissibility of a single degree-of-freedom system

5. Speed measurement of a DC motor using various sensors

6. Speed torque characteristic of DC servomotor

7. Measurement of linear displacement by potentiometer & PID demo

(a) Ball & beam control system; PID demo

(b) Magnetic levitation control system

B.


1. Impact of jet on flat and curved surfaces

2. Measurement of drag on a circular cylinder in high Reynolds number flow (wake survey method)

3. Energy loss measurements in subcritical and supercritical open channel flow

4. Measurement of fluid viscosity (air and oil)

5. Determination of friction factor as a function of Reynolds number in pipe flow

6. Studying laminar-turbulent transition for flow in a tube

7. Boundary layer flow over a flat plate

8. Pressure distribution around a circular cylinder in high Reynolds number flow

C.


1. Hardness test

2. Fatigue test

3. Tensile test (UTM)

4. Ellipsometry

5. Impact excitation


Proposing instructors: DUGC, ME

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