Statistical Modelling for Machine Learning detailed Syllabus for Data Science (DS), K scheme PDF has been taken from the MSBTE official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below.
For all other MSBTE Data Science 3rd Sem K Scheme Syllabus PDF, do visit MSBTE Data Science 3rd Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for statistical modelling for machine learning is as follows.
Rationale
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Course Outcomes:
Students will be able to achieve & demonstrate the following COs on completion of course based learning
- Solve the given problem based on Statistic Techniques using R-Programming.
- Implement Statistic methods using R-Programming.
- Use Principles of Probability to solve given Problem.
- Implement appropriate method based on the Interpolation.
- Apply Sampling Methods to solve given problem using R-Programming.
Unit I
Statistical Techniques 1.1 Frequency Distribution: Definition, Basic terms. 1.2 Classification of Data: Raw, Ungroup and Group data. 1.3 Measures of Central Tendency: Mean, Median and Mode for all types of data. 1.4 Concept of Quartiles, Deciles and Percentiles for all types of data. 1.5 Geometric mean and Harmonic mean and Combined mean for given data. 1.6 Graphical Representation to find Mode (Histogram) and Median (Ogive curve ). 1.7 Measures of Dispersion: Range, Mean Deviation, Standard Deviation, Variance. 1.8 Skewness: Types of skewness, Test of skewness, Co-efficient of skewness-Karl Pearson’s and Bowley’s coefficient. 1.9 Types of skewness in terms of Mean and Mode. 1.10 Measures of Kurtosis using central moment. Classroom Lecture Flipped Classroom Demonstration
Unit II
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Unit III
Probability of Random Variable 3.1 Probability : Definition, Terminologies. 3.2 Theorem of Probability: Addition, Multiplication. 3.3 Conditional probability. 3.4 Bayes’ theorem. Classroom Lecture Flipped Classroom Demonstration
Unit IV
Interpolation 4.1 Introduction. 4.2 Lagrange’s Interpolation formula. 4.3 Finite Differences: Forward difference, Backward difference, Shift operator, Inverse shift operator. 4.4 Relation between forward, backward, shift and inverse shift operator. 4.5 Newton’s Gregory forward and backward difference Interpolation Formula. 4.6 Concept of Extrapolation. Classroom Lecture Flipped Classroom Presentations
Unit V
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Suggested Micro Project / Assignment
Assignment
- Collect data of at least 05 real world examples and test the Hypothesis of sampling distribution.
- Collect data of at least 05 real world examples and calculate Measures of skewness and kurtosis and prepare the document.
- Collect data of at least 05 real world examples and draw/fit straight line and second-degree polynomial.
- Collect data of at least 05 real world examples and calculate probability using Bayes’ theorem.
- Collect data of at least 03 city like cost of living and temperature data etc. and interpolate the missing index number for it and prepare the document.
Micro project
- Analyze Uber Data: Analyze different parameters like the number of trips made in a day, the number of trips during a particular month, average passenger that uber can have in a day, the peak hours where more customer available, maximum number of trips found on day of the month, etc.
- Implement each least squares regression technique using a programming language such as Python or R. Utilize libraries like scikit-learn or stats models for implementation, ensuring proper parameter tuning and regularization settings for each technique.
- Design a simple hypothesis testing scenario where you simulate data under different conditions and perform chisquare tests to assess the significance of observed difference.
- Perform case Study on probabilistic model for predicting relations in social websites system.
- Above is just a suggestive list of microprojects and assignments; faculty must prepare their own bank of microprojects, assignments, and activities in a similar way.
- The faculty must allocate judicial mix of tasks, considering the weaknesses and / strengths of the student in acquiring the desired skills.
- If a microproject is assigned, it is expected to be completed as a group activity.
- SLA marks shall be awarded as per the continuous assessment record.
- For courses with no SLA component the list of suggestive microprojects / assignments/ activities are optional, faculty may encourage students to perform these tasks for enhanced learning experiences.
- If the course does not have associated SLA component, above suggestive listings is applicable to Tutorials and maybe considered for FA-PR evaluations.
Collect temperature data from different locations at various times of the day. Use interpolation techniques such as linear interpolation or spline interpolation to estimate the temperature at specific times and locations where data is not available.
Note :
Laboratory Equipment
- Open-source software like SageMaths, MATHS3D, GeoGebra, Graph, DPLOT, and Graphing Calculator ( Graph Eq 2.13), ORANGE can be used for Graph theory and tree, Statistics respectively. All
- Minimum Hardware requirement: Personal computer, (i3-i5 preferable), RAM minimum 4 GB onwards. All
- Minimum system requirement: 64-bit operating system such as Windows 10, macOS 10.13, or a recent version of Linux. All
- Software: R-Studio All
Suggested Learning Materials
For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier..
Learning Websites & Portals
- http://nptel.ac.in/courses/106102064/1 Online Learning Initiatives by IITs and IISc
- https://ocw.mit.edu/ MIT Open courseware
- https://www.khanacademy.org/math Concept of Mathematics through video lectures and notes
- www.mathworks.com/ Applications of concepts of Mathematics to coding.
- https://amser.org/b920509/science–mathematics AMSER (Interpolation. Extrapolation
- https://www.coursera.org/learn/r-programming R Programming: Free online Course 0
- https://libguides.furman.edu/oer/subject/mathematics Open Education Resources (OER) in Mathematics. ( Interpolation. Extrapolation
- https://www.wolframalpha.com/ Solving Mathematical Problems, performing calculations, visualizing mathematical concepts.
- https://brilliant.org/ Interactive Learning in Mathematics
- https://www.w3resource.com/r-programming-exercises/basic/ R Programming Basic, Exercises, Practice, Solution
- www.datamentor.io/r-programming/examples/ R Programming Examples
- https://www.tutorialspoint.com/r_programming_language/index. asp R-Programming Online Course
- https://www.freecodecamp.org/news/all-the-math-you-need-in-a rtificial-intelligence/ Mathematics in AI
- https://byjus.com/maths/least-square-method/ Least Square Method
- https://www.w3resource.com/r-programming-exercises/basic/r-p rogramming-basic-exercise-3.php R-Programming: Basic Exercises with Solution
Note : Teachers are requested to check the creative common license status/financial implications of the suggested online educational resources before use by the students
For detail Syllabus of all other subjects of Data Science, K scheme do visit Data Science 3rd Sem Syllabus for K scheme.
For all Data Science results, visit MSBTE Data Science all semester results direct links.