MSBTE K-scheme · Computer
MSBTE K-scheme Artificial Intelligence and Machine Learning (AN) syllabus: subjects and course codes
Every course in Diploma in Artificial Intelligence and Machine Learning, semester by semester, with the official course code, abbreviation, type, credits and total marks.
What are the subjects in MSBTE K-scheme Artificial Intelligence and Machine Learning?
MSBTE K-scheme Diploma in Artificial Intelligence and Machine Learning (programme code AN) has six semesters. Semester 1 includes Basic Mathematics (311302), Communication Skills (English) (311303), Basic Science (Physics / Chemistry) (311305), Fundamentals of ICT (311001). Semester 2 includes Applied Mathematics (312301), Basic Electrical and Electronics Engineering (312302), Programming in C (312303), Linux Basics (312001). The full semester-wise list with course codes, credits and marks is below.
Semester 1 7 courses
| Course title | Abbr. | Code | Type | Credits | Total marks |
|---|---|---|---|---|---|
| Basic Mathematics | BMS | 311302 | AEC | 4 | 125 |
| Communication Skills (English) | ENG | 311303 | AEC | 3 | 150 |
| Basic Science (Physics / Chemistry) | BSC | 311305 | DSC | 5 | 250 |
| Fundamentals of ICT | ICT | 311001 | SEC | 2 | 75 |
| Engineering Workshop Practice (Computer Group) | WPC | 311002 | SEC | 2 | 100 |
| Yoga and Meditation | YAM | 311003 | VEC | 1 | 50 |
| Engineering Graphics (Electronics, Computer and Allied Branches) | EGP | 311008 | DSC | 3 | 100 |
Semester 2 7 courses
| Course title | Abbr. | Code | Type | Credits | Total marks |
|---|---|---|---|---|---|
| Applied Mathematics | AMS | 312301 | AEC | 2 | 100 |
| Basic Electrical and Electronics Engineering | BEE | 312302 | AEC | 5 | 250 |
| Programming in C | PIC | 312303 | AEC | 5 | 225 |
| Linux Basics | BLP | 312001 | DSC | 2 | 50 |
| Professional Communication | PCO | 312002 | SEC | 1 | 50 |
| Social and Life Skills | SFS | 312003 | VEC | 1 | 50 |
| Web Page Designing | WPD | 312004 | SEC | 4 | 125 |
Semester 3 6 courses
| Course title | Abbr. | Code | Type | Credits | Total marks |
|---|---|---|---|---|---|
| Database Management System | DMS | 313302 | DSC | 5 | 200 |
| Digital Techniques | DTE | 313303 | DSC | 3 | 175 |
| Data Structure using Python | DSP | 313306 | DSC | 4 | 175 |
| Statistical Modelling for Machine Learning | SML | 313307 | DSC | 5 | 200 |
| Essence of Indian Constitution | EIC | 313002 | VEC | 1 | 50 |
| Data Story Telling and Visualization | DST | 313004 | DSC | 2 | 50 |
Semester 4 6 courses
| Course title | Abbr. | Code | Type | Credits | Total marks |
|---|---|---|---|---|---|
| Environmental Education and Sustainability | EES | 314301 | VEC | 2 | 125 |
| Java Programming | JPR | 314317 | AEC | 5 | 200 |
| Data Communication and Computer Network | DCN | 314318 | DSC | 4 | 175 |
| Mathematics for Machine Learning | MML | 314320 | AEC | 3 | 150 |
| Microprocessor Programming | MIC | 314321 | DSC | 3 | 175 |
| UI/UX Design | UID | 314005 | SEC | 3 | 75 |
Semester 5 8 courses
| Course title | Abbr. | Code | Type | Credits | Total marks |
|---|---|---|---|---|---|
| Operating System | OSY | 315319 | DSC | 3 | 175 |
| AI & Ml Algorithm | AMA | 315330 | DSE | 3 | 175 |
| Entrepreneurship Development and Startups | ENDS | 315002 | AEC | 1 | 75 |
| Seminar and Project Initiation Course | SPI | 315003 | AEC | 1 | 75 |
| Internship (12 weeks) | ITR | 315004 | INP | 10 | 200 |
| Elective 1 (Any - One) | |||||
| Advance Database Management | ADM | 315324 | DSE | 2 | 150 |
| Cloud Computing for Data Science | CCD | 315327 | DSE | 2 | 150 |
| Natural Language Processing | NLP | 315329 | DSE | 2 | 150 |
Semester 6 9 courses
| Course title | Abbr. | Code | Type | Credits | Total marks |
|---|---|---|---|---|---|
| Management | MAN | 315301 | AEC | 2 | 125 |
| Big Data Analytics | BDA | 316318 | DSC | 4 | 150 |
| Principles of Image Processing | PIP | 316319 | DSC | 2 | 125 |
| Mobile Application Development | MAD | 316006 | DSC | 4 | 75 |
| Network Managment and Administration | NMA | 316007 | AEC | 3 | 50 |
| Capstone Project | CPE | 316004 | INP | 2 | 150 |
| Elective 2 (Any - One) | |||||
| Advanced Algorithm in AI & Ml | AAM | 316320 | DSE | 3 | 175 |
| Data Warehousing with Mining Techniques | DWM | 316321 | DSE | 3 | 175 |
| Reinforcement Learning | RFL | 316323 | DSE | 3 | 175 |
Highlighted rows are internship, seminar and project courses. Data source: MSBTE curriculum search portal, retrieved 2026-10-09. For the detailed curriculum of a course, open it on the official portal. This page is a student-friendly reference, not an official document.
Internship and project courses
| Course | Code | Semester | Credits | Marks | Help |
|---|---|---|---|---|---|
| Seminar and Project Initiation Course | 315003 | 5 | 1 | 75 | Guidelines |
| Internship (12 weeks) | 315004 | 5 | 10 | 200 | ITR format |
| Capstone Project | 316004 | 6 | 2 | 150 | Black book format |
How K-scheme marks work
- FA-TH is the average of two class tests of 30 marks each.
- SA-TH is the end-semester theory examination.
- FA-PR and SA-PR are continuous and end-semester practical assessment. A student who does not secure minimum marks in FA-PR of any course is declared detained in that semester.
- SLA is self-learning assessment (assignments, micro-projects). Failing SLA means the work must be repeated and resubmitted.
- One credit equals 30 notional learning hours.
FAQs
Which semester has the internship in MSBTE Artificial Intelligence and Machine Learning?
Semester 5: Internship (12 weeks), abbreviation ITR, course code 315004, 10 credits and 200 marks.
What is the capstone project code for MSBTE Artificial Intelligence and Machine Learning?
Capstone Project is course code 316004 (CPE) in semester 6, with 2 credits and 150 marks.
How many subjects are there in each semester of Artificial Intelligence and Machine Learning?
Semester 1: 7 compulsory courses; Semester 2: 7 compulsory courses; Semester 3: 6 compulsory courses; Semester 4: 6 compulsory courses; Semester 5: 5 compulsory courses plus electives; Semester 6: 6 compulsory courses plus electives.
What is the programme code of Artificial Intelligence and Machine Learning in MSBTE?
The MSBTE programme code is AN.
What do AEC, DSC, SEC, VEC, DSE and INP mean?
They are course types: AEC is Ability Enhancement Course, DSC is Discipline Specific Core, SEC is Skill Enhancement Course, VEC is Value Education Course, DSE is Discipline Specific Elective and INP covers internship and project courses.
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