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AKTU Introduction to AI & Machine Learning (MNAM401) Syllabus PDF – B.Tech Minor Degree in Artificial Intelligence and Machine Learning
Introduction to AI & Machine Learning MNAM401
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- Semester 4
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Official AKTU syllabus of Introduction to AI & Machine Learning (MNAM401) for B.Tech Minor Degree in Artificial Intelligence and Machine Learning – 4 credits, L-T-P 3-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Introduction to AI & Machine Learning
Download PDFMNAM401 · 4 credits · L-T-P 3-1-0
Course Outcomes
At the end of course , the student will be able to understand
| CO 1 | Provide you with the knowledge and expertise to become a proficient data scientist. |
| CO 2 | Demonstrate an understanding of statistics and machine learning concepts that are vital for data science |
| CO 3 | The student will learn to apply knowledge representation techniques and problem solving strategies to common AI applications. Produce Python code to statistically analyse a dataset; |
| CO 4 | Critically evaluate data visualisations based on their design and use for communicating stories from data; |
Course Content
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Defining Artificial Intelligence, Defining AI techniques, Using Predicate Logic and Representing Knowledge as Rules, Representing simple facts in logic, Computable functions and predicates, Procedural vs Declarative knowledge, Logic Programming, Mathematical foundations: Matrix Theory and Statistics for Machine Learning. | 12 |
| II | Idea of Machines learning from data, Classification of problem –Regression and Classification, Supervised and Unsupervised learning. | 08 |
| III | Linear Regression: Model representation for single variable, Single variable Cost Function, Gradient Decent for Linear Regression, Gradient Decent in practice. | 10 |
| IV | Logistic Regression: Classification, Hypothesis Representation, Decision Boundary, Cost function, Advanced Optimization, Multi-classification (One vs All), Problem of Over fitting. | 07 |
| V | Discussion on clustering algorithms and use-cases cantered around clustering and classification. | 05 |
Text Books
- Saroj Kaushik, Artificial Intelligence, Cengage Learning, 1st Edition 2011.
- Anindita Das Bhattacharjee, “Practical Workbook Artificial Intelligence and Soft Computing for beginners, Shroff Publisher-X team Publisher.
- Yuxi (Hayden) Liu, “Python Machine Learning by Example”, Packet Publishing Limited, 2017.
- Tom Mitchell, Machine Learning, McGraw Hill, 2017.
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2011.
- T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning, 2e, 2011. Corresponding Online Resources:
- Artificial Intelligence,https://swayam.gov.in/nd2_cec20_cs10/preview.
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