CSC290: Introduction to Artificial Intelligence
Classes: Monday – Wednesday 2.40 – 4.00 pm
Location: Ford Hall 342
Instructor: Dr. Şadi Evren ŞEKER (Office: Ford Hall 252)
Office Hours
- Tuesday, 13.00 – 15.00
- Other times by appointment/as available
- Lunch meetings available by request for small groups
E-Mail: ai@sadievrenseker.com
Web Site: http://sadievrenseker.com/wp/?p=1172
Course Content:
- History and Philosophy of the Artificial Intelligence (AI)
- Classical AI approaches like search problems, machine learning, constraint satisfaction, graphical models, logic etc.
- Learning how to model a complex real-world problem by the classical AI approach
Objectives:
- Introduction to Artificial Intelligence Problems
- Programming with a mathematical notation language (like a lisp variant, scheme)
- Writing a real world application with an AI module (like a game)
- Introducing sub-AI topics like neural computing, uncertainity and bayesian networks, concept of learning (supervised / unsupervised) etc.
Texts:
- S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall
- A must check : http://aima.cs.berkeley.edu
- Some parts of the course is related to Machine Learning, Data Science, Data Mining, Pattern Recognition, Natural Language Processing, Statistics, Logic, Artificial Neural Networks and Fuzzy Logic, so you can read any [text] books about the topics.
Grading: Programming assignment / Homeworks (30%), Midterm Exam (20%), Final Exam (50%)
Midterm and Final Exams (take home for 24 hours)
Course Outline:
- Introduction and Agents (chapters 1,2)
- Search (chapters 3,4,5,6)
- Logic (chapters 7,8,9)
- Planning (chapters 11,12)
- Uncertainty (chapters 13,14)
- Learning (chapters 18,20)
- Natural Language Processing (chapter 22,23)
Schedule and Contents (Very Very Very Tentative):
- Class 1, Jan 30 : Introduction : Course Demonstration Slides, Introduction Slides
- Class 2, Feb 1: Agents
- Class 3, Feb 6: Search
- Class 4, Feb 8: Introduction to Scheme 1, Search Homework 1 (Due Date: TBA)
- Class 5, Feb 13: Heuristic Search
- Class 6, Feb 15: Scheme Practice 2, Heuristic Homework 2 (Due Date: TBA)
- Class 7, Feb 20: Constraint Satisfaction Problems
- Class 8, Feb 22: Scheme Practice 3, CSP Homework 3 (Due Date: TBA)
- Class 9, Feb 27: Game Playing
- Class 10, Mar 1: Scheme Practice 4, Game Homework 4 (Due Date: TBA)
- Class 11, Mar 6: Midterm
- Class 12, Mar 8: Midterm Solutions
- Mar 13, 15: No Classes , Spring Recess
- Class 10, Mar 20: Logic
- Class 11, Mar 22: First Order Logic
- Class 12, Mar 27: Inference in First Order Logic
- Class 13, Mar 29: Scheme Practice 5, Logic Homework 5 (Due Date: TBA)
- Class 14, Apr 3: Uncertainity and Fuzzy Logic
- Class 15, Apr 5: Machine Learning and Problems
- Class 16, Apr 10: Supervised / Unsupervised Learning and Classification / Clustering Problems, k-nn and k-means
- Class 17, Apr 12: Naive Bayes, Decision Trees, Rule Based Learning, Error Calculation
- Class 18, Apr 17: Scheme Practice 6, ML Homework 6 (Due Date: TBA)
- Class 19, Apr 19: Prediction, Regression and Association Rule Mining
- Class 20, Apr 24: Artificial Neural Networks
- Class 21, Apr 26: Natural Language Processing
- Class 22, May 1: Final Exam
- Class 23, May 3: Final Exam Solutions
Collaboration Policy: You may freely use internet resources and your course notes in completing assignments and quizzes for this course. You may not consult any person other than the professor when completing quizzes or exams. (Clarifying questions should be directed to the professor.) On assignments you may collaborate with others in the course, so long as you personally prepare the materials submitted under your name, and they accurately reflect your understanding of the topic. Any collaborations should be indicated by a note submitted with the assignment.
Announcements
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