Session |
Topic |
Topic details |
Readings |
Slides |
Exercises |
1 |
Introduction |
What is AI?, goals of AI, AI applications, foundations of AI, history of AI |
1 |
|
|
2 |
Intelligent Agents |
Agents and Environment, Nature of Environment, Structure of Agent, Rationality, Performance Measures |
2 |
|
|
3, 4 |
Problem Solving Agents and Search |
Problem Solving Agents, Best-First Search, Uninformed Search: Breadth First Search, Depth First Search, Iterative Deepening DFS |
3.1-3.4 |
|
|
5 |
Informed Search |
Heuristic Functions, Greedy Best First search, A* Search |
3.5-3.6 |
|
|
6, 7 |
Constraint Satisfaction Problems |
CSP, Inference in CSPs, Backtracking and Local Search for CSPs |
6.1-6.5 |
|
|
8, 9, 10 |
Symbolic AI and Logical Agents |
Introduction to Logic, Propositional Logic (Introduction, Theorem Proving, Model Checking), First-Order Logic (Introduction, Syntax and Semantics), Knowledge-based Agents |
7.1-7.7, 8.1-8.4 |
|
|
|
Mid-Semester Exam |
Topics covered till date |
|
|
|
11 |
Probability Review |
Basics, Conditional Probability, Bayes' Theorem, Naive Bayes Classifier |
12 |
|
|
12, 13 |
Probabilistic Reasoning |
Bayesian Networks, Representation, Conditional Independence, Exact Inference (Enumeration and Variable Elimination), Approximate Inference |
13.1-13.5 |
|
|
14 |
Probabilistic Reasoning over Time - I |
Time and Uncertainty, Markov Chains, Hidden Markov Models (HMMs) |
14.1-14.3 |
|
|
15 |
Probabilistic Reasoning over Time - II |
HMM Algorithms: filtering, smoothing, prediction |
14.4-14.5 |
|
|
16 |
Utility Theory |
Utility Theory, Utility Functions, Decision Networks, Value of Perfect Information |
16.1-16.6 |
|
|
17 |
Sequential Decision Making - I |
Markov Decision Processes (MDPs): States, Actions, Rewards, Transition Function, Policy |
17.1 |
|
|
18 |
Sequential Decision Making - II |
MDP Algorithms: Value Iteration, Policy Iteration, Bandit Problem |
17.2-3 |
|
|
19 |
Sequential Decision Making - III |
Partially Observable MDPs (POMDPs): Belief States, POMDP Algorithms |
17.4 |
|
|
20 |
Machine Learning |
Fundamentals, Linear Regression |
19.1-6 |
|
|
21, 22 |
Reinforcement Learning |
Introduction to Reinforcement Learning (RL), Agent-Environment Interaction, Learning from Rewards, Passive and Active Reinforcement Learning |
22 |
|
|
23 |
Project presentations |
|
|
|
|
24 |
Epilogue |
Summarisation, Quiz |
|
|
|
AIMA V3 Python libraries and notebooks can be downloaded
here or here
Installation instructions if downloaded from the second repository: Extract and navigate to the folder containing the file
setup.py. Then run:
pip install -r requirements.txt
pip install .The libraries can be used as:
from aima3.logic import * etc.
AIMA Version 4 download and installation instructions here.