IT & Data Analysis Courses

Artificial Intelligence - Intermediate Level


Description
Artificial Intelligence (Intermediate to Advanced)
Welcome to Artificial Intelligence! In this course, you will learn the concepts of Artificial Intelligence (AI) and apply them to the design and implementation of intelligent agents that solve real-world AI problems, including problems in search, games, machine learning, logic, and constraint satisfaction. We will provide a broad understanding of the basic techniques for building intelligent computer systems. Topics include the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing and adversarial search, logical agents, constraint satisfaction problems, along with techniques in machine learning and other applications of AI, such as natural language processing (NLP). Course Level Please note this is a graduate level course. Expect to spend at least several hours to complete the programming assignments, although the exact amount of time will depend on your background and proficiency with coding. If you are taking this course for fun, and are not working towards a passing grade for credit, you can of course watch the lectures and answer the quizzes. Prerequisites Students are required to have the following prerequisites:
• Linear algebra (vectors, matrices, derivatives)
• Calculus
• Basic probability theory • Python programmingThe course offers an excellent opportunity for students to dive into Python while solving AI problems and learning its applications. Programming assignments will be in Python. Class Schedule
• Week 1: Introduction to AI, history of AI, course logistics, and roadmap
• Week 2: Intelligent agents, uninformed search
• Week 3: Heuristic search, greedy search, A* algorithm, stochastic search
• Week 4: Adversarial search, game playing
• Week 5: Machine Learning 1: basic concepts, linear models, K nearest neighbors, overfitting
• Week 6: Machine Learning 2: perceptrons, neural networks, naive Bayes
• Week 7: Machine Learning 3: decision trees, ensemble, logistic regression, and unsupervised learning
• Week 8: Constraint satisfaction problems
• Week 9: AI applications to vision/robotics, Course Review and Conclusion Assignments There will be two kinds of assignments: Quizzes (conceptual): These test your understanding of the lectures. You may be asked to reason abstractly about the nature of an algorithm, or to perform a technique by hand on an small problem. Please read the instructions carefully, note any formatting requirements, and review your answers before hitting submit. Except for the most challenging questions, you will often only have one attempt to answer a question.
Projects (programming): These offer an excellent opportunity for you to dive into Python programming and design while solving AI problems and learning its applications. You will often be presented with a general problem and asked to come up with solutions to the problem by implementing algorithms from scratch. As mentioned above, expect to spend at least several hours to complete the programming assignments.
Content
  • 1. Overview of AI
  • 2. Application of AI
  • 3. AI Foundation and History
  • 4. Course Overview
  • 5. Intelligent Agents
  • 6. Types of Intelligent Agents
  • 7. Search Agents
  • 8. Examples of Problem Formulation
  • 9. State Space vs. Search Space
  • 10. Tree Search and Examples of Search Agents
  • 11. Breadth-First Search
  • 12. Depth-First Search
  • 13. DLS and Uniform-cost Search
  • 14. Uniform-Cost Search Criteria
  • 15. Toy Example
  • 16. Map Example
  • 17. Introduction of Informed Search
  • 18. Informed Search
  • 19. A Search
  • 20. A Optimality
  • 21. Search Algorithms Recap
  • 22. Local Search
  • 23. Adversarial Search
  • 24. The Minimax Algorithm
  • 25. Alpha-Beta Pruning
  • 26. Alpha-Beta Pruning Move Ordering
  • 27. Stochastic Games
  • 28. Introduction to Machine Learning
  • 29. Supervised and Unsupervised Learning
  • 30. K-nearest Neighbors and Training-Testing
  • 31. Overfitting Underfitting
  • 32. Regularization
  • 33. Linear Regression Model
  • 34. Normal Equation and Gradient Descent
  • 35. Classification and Perceptron Algorithm
  • 36. Logistic Regression
  • 37. Tree Classifiers Example
  • 38. Overfitting Data
  • 39. Bayes Rule
  • 40. Naive Bayes Classifier
  • 41. Ensemble Methods Boosting
  • 42. AdaBoost Algorithm
  • 43. Perceptron to MLP
  • 44. The XOR Example
  • 45. Backpropagation Algorithm
  • 46. Backpropagation Rules
  • 47. Backpropagation Algorithm Example
  • 48. Unsupervised Learning
  • 50. Association Rules
  • 51. Breadth First Search and Depth First Search
  • 52. Probabilistic Interpretation of Association Rules
  • 53. Multi-Dimensional Rules
  • 54. Quantitative AR
  • 55. Constraint Satisfaction Problems
  • 56. Cryptarithmetic Puzzle
  • 57. Backtracking
  • 58. Constraint Propagation
  • 59. Problem Structure
  • 60. Deep Learning Background and History
  • 61. Deep Learning Architecture and Application
  • 62. Robot Path Planning-Visibility Graphs
  • 63. Voronoi Graphs and Potential Fields
  • 64. Probabilistic Roadmap Planner _PRM_
  • 65. Rapidly-Exploring Random Tress _RRT_ and Path Planning Summary
  • 66. Course Review and Conclusion
Completion rules
  • All units must be completed
  • Leads to a certification with a duration: Forever