18CS71 Artificial Intelligence and Machine Learning syllabus for CS

What is artificial intelligence?, Problems, problem spaces and search, Heuristic search techniques

Texbook 1: Chapter 1, 2 and 3

RBT: L1, L2

Knowledge representation issues, Predicate logic, Representaiton knowledge using rules. Concpet Learning: Concept learning task, Concpet learning as search, Find-S algorithm, Candidate Elimination Algorithm, Inductive bias of Candidate Elimination Algorithm.

Texbook 1: Chapter 4, 5 and 6

Texbook2: Chapter 2 (2.1-2.5, 2.7)

RBT: L1, L2, L3

Decision Tree Learning:

Introduction, Decision tree representation, Appropriate problems, ID3 algorith. Aritificil Nueral Network: Introduction, NN representation, Appropriate problems, Perceptrons, Backpropagation algorithm.

Texbook2: Chapter 3 (3.1-3.4), Chapter 4 (4.1-4.5)

RBT: L1, L2, L3

Bayesian Learning:

Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting, MDL principle, Bates optimal classifier, Gibbs algorithm, Navie Bayes classifier, BBN, EM Algorithm

Texbook2: Chapter 6

RBT: L1, L2, L3

Instance-Base Learning: Introduction, k-Nearest Neighbour Learning, Locally weighted regression, Radial basis function, Case-Based reasoning. Reinforcement Learning: Introduction, The learning task, Q-Learning.

Texbook 1: Chapter 8 (8.1-8.5), Chapter 13 (13.1 – 13.3)

RBT: L1, L2, L3

Course Outcomes:

The student will be able to :

• Appaise the theory of Artificial intelligence and Machine Learning.
• Illustrate the working of AI and ML Algorithms.
• Demonstrate the applications of AI and ML.

Question Paper Pattern:

• The question paper will have ten questions.
• Each full Question consisting of 20 marks
• There will be 2 full questions (with a maximum of four sub questions) from each module.
• Each full question will have sub questions covering all the topics under a module.
• The students will have to answer 5 full questions, selecting one full question from each module.

Textbooks:

1. Tom M Mitchell,“Machine Lerning”,1 st Edition, McGraw Hill Education, 2017.

2. Elaine Rich, Kevin K and S B Nair, “Artificial Inteligence”, 3 rd Edition, McGraw Hill Education, 2017.

Reference Books:

1. Saroj Kaushik, Artificial Intelligence, Cengage learning

2. Stuart Rusell, Peter Norving , Artificial Intelligence: A Modern Approach, Pearson Education 2nd Edition

3. AurÈlienGÈron,"Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems", 1st Edition, Shroff/O'Reilly Media, 2017.

4. Trevor Hastie, Robert Tibshirani, Jerome Friedman, h The Elements of Statistical Learning, 2nd edition, springer series in statistics.

5. Ethem Alpaydın, Introduction to machine learning, second edition, MIT press

6. Srinvivasa K G and Shreedhar, “ Artificial Intelligence and Machine Learning”, Cengage