Artificial Intelligence Application Development and Jira Notes | B.Sc. (Information Technology) Semester 5 | Mumbai University | munotes

Contents

Module I Introduction to AI and Intelligent Agents

1. How This Paper Is Examined, and What Your Journal Must Contain 1
2. The Laboratory: Python and the Four Libraries This Paper Needs 5
3. The Python You Need Before the First Practical 8
4. What Makes an Agent Rational, and What PEAS Is For 11
5. PEAS for the Vacuum-Cleaner World 14
6. PEAS for the Autonomous Taxi 17
7. Classifying a Task Environment 20
8. The Table-Driven Agent in Python 23
9. Why the Table Does Not Scale, and What Replaces It 26
10. The State Space, the Node and the Frontier 29
11. The 8-Puzzle: States, Moves and the Goal 33
12. Breadth-First Search on the 8-Puzzle 37
13. What Breadth-First Search Costs 41
14. The Water Jugs, Solved by Depth-First Search 44
15. Changing the Capacities, and When There Is No Solution 48
16. The Romania Map as a Weighted Graph 52
17. Uniform-Cost Search from Arad to Bucharest 56
18. A Second Graph for Uniform-Cost Search 60
19. Heuristics, and What Admissible Means 63
20. Greedy Best-First Search on the 8-Puzzle 67
21. A-Star Search on the 8-Puzzle 71
22. Greedy Against A-Star, on the Same Puzzle, Counted 75
23. A-Star on the Romania Map with Straight-Line Distance 78
24. Bayes' Rule, Worked by Hand 82
25. The Medical Test: Writing the Program 85
26. Why the Answer Feels Wrong: Base Rates and a Second Test 89
27. Naive Bayes: The Model Behind the Classifier 92
28. A Naive Bayes Classifier on the Weather Dataset 96
29. A Naive Bayes Spam Classifier 100
30. Bayesian Networks: The Burglary and Alarm Model 104
31. Building the Network with pgmpy 108
32. Exact Inference: P(Burglary given Alarm = True) 112
33. Checking the Network's Answer by Hand 116
34. What a Machine Learning Program Actually Does 120
35. Loading a Dataset with pandas 123
36. Preprocessing: Missing Values and Scaling 126
37. Splitting the Data, and Why Training Accuracy Lies 130
38. The Whole Workflow in One Program 134
39. Linear Regression with scikit-learn 137
40. Reading a Regression's Numbers 140
41. MNIST, and a Binary Classifier for One Digit 143
42. The Confusion Matrix, Precision and Recall 147
43. The Trade-off, and the ROC Curve 151
44. k-Fold Cross-Validation 155
45. Decision Trees: How the Split Is Chosen 159
46. A Decision Tree Classifier on Iris 163
47. Decision Tree Regression 166
48. The k-Nearest Neighbours Classifier 169
49. Multiclass Classification 173
50. Tuning a Model Without Fooling Yourself 177
51. Putting a Model to Work 181
52. Looking at a Dataset for Bias 185
53. Class Imbalance, and the Score That Hides It 190
54. Fairness: What the Numbers Can and Cannot Tell You 194

Module II Jira Setup & Project Configuration, Advanced Configuration,

1. The Sources This Half Is Written From 198
2. Agile, Scrum and Kanban: The Vocabulary Jira Implements 201
3. What Jira Cloud Is: Site, Product, Project 205
4. Creating the Account and Reading the Dashboard 208
5. Company-Managed and Team-Managed: Which One to Create 211
6. Creating the Scrum or Kanban Project 214
7. Configuring the Project's Details 217
8. Adding Team Members 220
9. Project Roles, and What Assigning One Does 223
10. Issue Types: Epic, Story, Task, Bug 226
11. Components and Versions 229
12. Creating Issues Under an Epic 232
13. Assignee, Priority and Due Date 235
14. What a Workflow Is 238
15. Designing the Custom Workflow 241
16. Conditions, Validators and Post Functions 244
17. Publishing the Workflow and Associating It 247
18. The Scrum Board, and Configuring It 250
19. Estimation: Story Points and the Backlog 253
20. Creating and Starting a Sprint 257
21. Completing a Sprint and Reading the Sprint Report 260
22. The Kanban Board, WIP Limits and Flow 264
23. Tracking Progress: The Control Chart and Cycle Time 267
24. JQL: The Query Language 271
25. Writing Basic JQL Queries 274
26. Saving a Filter and Sharing It 277
27. The Burndown Chart, Read and Recomputed 280
28. The Velocity Chart 284
29. The Pie Chart and Issue Statistics 287
30. A Custom Dashboard and Its Gadgets 290
31. Permissions: The Scheme Behind the Buttons 293
32. Roles, Groups and Who Can Do What 296
33. Automation: Trigger, Condition, Action 299
34. Configuring and Testing a Rule 302
35. Jira on a Phone 305
36. The Practical Examination: The Slip, the Journal and the Viva 308