08.B - Syllabus of AI & DS PDF

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Syllabus of AI & DS...

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ANNA UNIVERSITY, CHENNAI AFFILIATED INSTITUTIONS B.TECH. ARTIFICAL INTELLIGENCE AND DATA SCIENCE REGULATIONS – 2017 CHOICE BASED CREDIT SYSTEM

PROGRAM EDUCATIONAL OBJECTIVES (PEOs) 1. To provide graduates with the proficiency to utilize the fundamental knowledge of basic sciences, mathematics, Artificial Intelligence, data science and statistics to build systems that require management and analysis of large volume of data. 2. To enrich graduates with necessary technical skills to pursue pioneering research in the field of AI and Data Science and create disruptive and sustainable solutions for the welfare of ecosystems. 3. To enable graduates to think logically, pursue lifelong learning and collaborate with an ethical attitude in a multidisciplinary team. PROGRAM OUTCOMES (POs) ENGINEERING GRADUATES WILL BE ABLE TO: 1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and Artificial Intelligence and Data Science basics to the solution of complex engineering problems. 2. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences. 3. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations. 4. Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions. 5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations. 6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice. 7. Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.

8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice. 9. Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings. 10. Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions. 11. Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one‘s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments. 12. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change. Programme Specific Outcomes 1. Graduates should be able to evolve AI based efficient domain specific processes for effective decision making in several domains such as business and governance domains. 2. Graduates should be able to arrive at actionable Fore sight, Insight , hind sight from data for solving business and engineering problems 3. Graduates should be able to create, select and apply the theoretical knowledge of AI and Data Analytics along with practical industrial tools and techniques to manage and solve wicked societal problems

ANNA UNIVERSITY, CHENNAI AFFILIATED INSTITUTIONS B.TECH. ARTIFICIAL INTELLIGENCE AND DATA SCIENCE REGULATIONS – 2017 CHOICE BASED CREDIT SYSTEM I - VIII SEMESTERS CURRICULUM

SEMESTER I SI. COURSE No CODE THEORY 1. HS8151 2. MA8151 3. PH8151 4. CY8151 5.

GE8151

6. GE8152 PRACTICALS 7. GE8161

8.

BS8161

COURSE TITLE

Communicative English Engineering Mathematics – I Engineering Physics Engineering Chemistry Problem Solving and Python Programming Engineering Graphics Problem Solving and Python Programming Lab Physics and Chemistry Lab

CATEG ORY

CONTACT PERIODS

L

T

P

C

HS BS BS

4 4 3

4 4 3

0 0 0

0 0 0

4 4 3

3

0

0

3

BS

3

ES

3

3

0

0

3

ES

6

2

0

4

4

ES

4

0

0

4

2

BS TOTAL

4 31

0 19

0 0

4 12

2 25

SEMESTER II SI.No

COURSE CODE

THEORY 1. HS8251 2. MA8252 3. AD8251 4. GE8291 5.

6.

BE8255

AD8252

PRACTICALS 7. GE8261 8. AD8261

CATEG ORY

CONTACT PERIODS

L

T

P

C

Technical English Linear Algebra Data Structures Design Environmental Science and Engineering Basic Electrical, Electronics, and Measurements Engineering Digital Principles and Computer Organization

HS BS PC

4 4 3

4 4 3

0 0 0

0 0 0

4 4 3

BS

3

3

0

0

3

ES

3

3

0

0

3

ES

5

3

0

2

4

Engineering Practices Lab Data Structures Design Lab

ES PC TOTAL

4 4 30

0 0 20

0 0 0

4 4 10

2 2 25

COURSE TITLE

SEMESTER III Sl. COURSE No. CODE THEORY 1. MA8351 2.

AD8301

3.

AD8302

4.

CS8392

5.

AD8351

PRACTICALS 6. AD8311 7.

CS8383

8.

HS8381

COURSE TITLE

Discrete Mathematics Introduction to Operating Systems Fundamentals of Data Science Object Oriented Programming Design and Analysis of Algorithms Data Science Laboratory Object Oriented Programming Laboratory Interpersonal Skills/Listening & Speaking

CATEG ORY

CONTACT PERIODS

BS

4

4

PC

5

PC

L

T

P

C

0

0

4

3

0

2

4

3

3

0

0

3

PC

3

3

0

0

3

PC

5

3

0

2

4

PC

4

0

0

4

2

PC

4

0

0

4

2

HS

2

0

0

2

1

TOTAL

30

16

0

14

23

SEMESTER IV Sl. COURSE N COURSE TITLE CODE o THEORY 1. MA8391 Probability and Statistics

CATEG ORY

CONTACT PERIODS

L

T

P

C

BS

4

4

0

0

4

2.

AD8401

Database Design and Management

PC

3

3

0

0

3

3.

AD8402

Artificial Intelligence I

PC

3

3

0

0

3

4.

AD8403

Data Analytics

PC

3

3

0

0

3

Professional Elective I

PE

3

3

0

0

3

Database Design and Management Laboratory Data Analytics Laboratory

PC

Artificial Intelligence – I Laboratory Advanced Reading and Writing

PC

5. PRACTICALS 6. AD8411 7.

AD8412

8.

AD8413

9.

HS8461

4

0

0

4

2

4

0

0

4

2

4

0

0

4

2

HS

2

0

0

2

1

TOTAL

30

16

2

14

23

PC

SEMESTER V Sl. COURSE COURSE TITLE No. CODE THEORY 1. AD8501 Optimization Techniques 2. CW8691 Computer Networks 3. AD8502 Data Exploration and Visualization 4. AD8551 Business Analytics 5. AD8552 Machine Learning 6. Open Elective I PRACTICALS 7. AD8511 Machine Learning Lab 8. AD8512 Mini Project on Data Sciences Pipeline

CATEG ORY

CONTACT PERIODS

PC PC

4 5

PC

5

PC PC OE

L

T

P

C

4 3 3

0 0 0

0 2 2

4 4 4

3 3 3

3 3 3

0 0 0

0 0 0

3 3 3

PC

4

PC

4

0 0

0 0

4 4

2 2

TOTAL

31

19

0

12

25

P

C

SEMESTER VI Sl. COURSE No CODE THEORY 1. AD8601 2. AD8602 3. IT8501 4. 5. PRACTICALS 6. IT8511 7. AD8611 8. HS8581 9. AD8612

CATEG ORY

CONTACT PERIODS

Artificial Intelligence II Data and Information Security Web Technology Professional Elective II Professional Elective III

PC PC PC PE PE

3 3 3 3 3

3 3 3 3 3

0 0 0 0 0

0 0 0 0 0

3 3 3 3 3

Web Technology Lab Artificial Intelligence - II Lab Professional Communication Socially relevant Project

PC PC HS PC TOTAL

4 4 2 4 29

0 0 0 0 15

0 0 0 0 0

4 4 2 4 14

2 2 1 2 22

COURSE TITLE

L

T

SEMESTER VII SI. COURSE No CODE THEORY 1. AD8701 2. AD8702 3. AD8703 4. AD8704 5. AD8705 6. PRACTICALS 7. AD8711 8. AD8712

COURSE TITLE

Deep Learning Text Analytics Basics of Computer Vision Big Data Management AI and Robotics Open Elective II Deep Learning Lab Mini Project on Analytics

CATE GORY

CONTACT PERIODS

PC PC PC PC PC OE

3 3 3 5 5 3

3 3 3 3 3 3

0 0 0 0 0 0

0 0 0 2 2 0

3 3 3 4 4 3

PC PC TOTAL

4 4 30

0 0 18

0 0 0

4 4 12

2 2 24

T

P

C

L

T

P

C

SEMESTER VIII Sl. COURSE No. CODE THEORY 1.

CATE GORY

CONTACT PERIODS

L

Professional Elective IV

PE

3

3

0

0

3

2.

Professional Elective V

PE

3

3

0

0

3

PRACTICALS 3. AD8811

Project Work

PC

20

0

0

20

10

TOTAL

26

6

0

20

16

COURSE TITLE

TOTAL NO. OF CREDITS: 183

PROFESSIONAL ELECTIVES (PE) SEMESTER IV, ELECTIVE - I SI. No

COURSE CODE

1.

EC8691

2. 3. 4. 5.

AD8001 AD8002 AD8003 AD8004

COURSE TITLE Microprocessors and Microcontrollers Software Development Processes Health care Analytics Mobile Applications Development Parallel Computing

CATE GORY

CONTACT PERIODS

L

T

P

C

PE

3

3

0

0

3

PE PE PE PE

3 3 3 3

3 3 3 3

0 0 0 0

0 0 0 0

3 3 3 3

SEMESTER VI, ELECTIVE - II SI. No 1.

COURSE CODE AD8005

CATE CONTACT GORY PERIODS

COURSE TITLE

L

T

P

C

2.

CW8591

Embedded Systems and Programming Software Architecture

3.

AD8006

Engineering Predictive Analytics

PE

3

3

0

0

3

4.

CS8603

Distributed Systems

PE

3

3

0

0

3

5.

CS8072

Agile Methodologies

PE

3

3

0

0

3

PE

3

3

0

0

PE

3

3

0

0

3

3

SEMESTER VI, ELECTIVE - III SI. No 1. 2.

COURSE CODE CS8081 AD8007

3. 4. 5.

CS8791 CS8085 AD8008

COURSE TITLE Internet of Things Software Testing and Quality Assurance Cloud Computing Social Network Analytics Web Services and API Design

CATEG ORY PE

CONTACT L PERIODS 3 3

T

P

C

0

0

3

PE

3

3

0

0

3

PE PE PE

3 3 3

3 3 3

0 0 0

0 0 0

3 3 3

SEMESTER VIII, ELECTIVE - IV SI. No 1.

COURSE CODE AD8009

2.

AD8010

3. 4. 5.

AD8011 AD8012 AD8013

COURSE TITLE Operations and Supply Chain Management Speech Processing and Analytics Cyber Security Nonlinear Optimization Ethics and AI

CATEG ORY

CONTACT PERIODS

L

T

P

C

PE

3

3

0

0

3

PE

3

3

0

0

3

PE PE PE

3 3 3

3 3 3

0 0 0

0 0 0

3 3 3

CATEGORY

CONTACT PERIODS

L

T

P

C

PE

3

3

0

0

3

PE

3

3

0

0

3

PE

3

3

0

0

3

PE

3

3

0

0

3

PE

3

3

0

0

3

SEMESTER VIII, ELECTIVE - V SI. N o 1. 2.

COURSE CODE AD8014 AD8081

3. 4.

MG8591 AD8015

5.

AD8016

COURSE TITLE Engineering Economics Cognitive Science and Analytics Principles of Management Bio-inspired Optimization Techniques Information Extraction and Retrieval

HS8151

COMMUNICATIVE ENGLISH

L

T

P

C

4

0

0

4

OBJECTIVES: ● To develop the basic reading and writing skills of first year engineering and technology students. ● To help learners develop their listening skills, which will, enable them listen to lectures and comprehend them by asking questions; seeking clarifications. ● To help learners develop their speaking skills and speak fluently in real contexts. ● To help learners develop vocabulary of a general kind by developing their reading skills UNIT I SHARING INFORMATION RELATED TO ONESELF/FAMILY& FRIENDS 12 Reading- short comprehension passages, practice in skimming-scanning and predicting- Writingcompleting sentences- - developing hints. Listening- short texts- short formal and informal conversations. Speaking- introducing oneself - exchanging personal information- Language development- Wh- Questions- asking and answering-yes or no questions- parts of speech. Vocabulary development-- prefixes- suffixes- articles.- count/ uncount nouns. UNIT II

GENERAL READING AND FREE WRITING

12

Reading - comprehension-pre-reading-post reading- comprehension questions (multiple choice questions and /or short questions/ open-ended questions)-inductive reading- short narratives and descriptions from newspapers including dialogues and conversations (also used as short Listening texts)- register- Writing – paragraph writing- topic sentence- main ideas- free writing, short narrative descriptions using some suggested vocabulary and structures –Listening- telephonic conversations. Speaking – sharing information of a personal kind—greeting – taking leaveLanguage development – prepositions, conjunctions Vocabulary development- guessing meanings of words in context. UNIT III

GRAMMAR AND LANGUAGE DEVELOPMENT

12

Reading- short texts and longer passages (close reading) Writing- understanding text structureuse of reference words and discourse markers-coherence-jumbled sentences Listening – listening to longer texts and filling up the table- product description- narratives from different sources. Speaking- asking about routine actions and expressing opinions. Language developmentdegrees of comparison- pronouns- direct vs indirect questions- Vocabulary development – single word substitutes- adverbs. UNIT IV READING AND LANGUAGE DEVELOPMENT 12 Reading- comprehension-reading longer texts- reading different types of texts- magazines Writingletter writing, informal or personal letters-e-mails-conventions of personal email- Listening- listening to dialogues or conversations and completing exercises based on them. Speaking- speaking about oneself- speaking about one’s friend- Language development- Tenses- simple present-simple past- present continuous and past continuous- Vocabulary development- synonyms-antonymsphrasal verbs

UNIT V EXTENDED WRITING 12 Reading- longer texts- close reading –Writing- brainstorming -writing short essays – developing an outline- identifying main and subordinate ideas- dialogue writing-Listening – listening to talksconversations- Speaking – participating in conversations- short group conversations-Language development-modal verbs- present/ past perfect tense - Vocabulary development-collocationsfixed and semi-fixed expressions. TOTAL: 60 PERIODS OUTCOMES: AT THE END OF THE COURSE, LEARNERS WILL BE...