CSC 290 : Introduction to Artificial Intelligence, Smith College, Department of Computer Science, Spring 2017

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


Web Site:

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


  • 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.


  • S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall
  • —A must check :
  • 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.

Course Prerequisites

In this course, you will get introduced to a new programming language Lisp, so it is essential that you have a good programming background (a good understanding of the content delivered at CSC111 course).

It is also highly recommended to get familiar with data structures (CSC212) and algorithms (CSC252) for two reasons. First, in some parts of the theoretical part of the course, some topics are related to data structures or algorithms. Second, these courses brings more programming skills which is highly required in programming part of the course.

Calculus MATH111, will come in handy in one or two places. Propositional logic and probability theory, are important prerequisites but they will be thoroughly reviewed in this course. As usual, ask me if you’re not sure about whether to take the course.


—Grading: Programming assignment / Homeworks (30%), —Midterm Exam (20%), Final Exam (50%)

—Midterm and Final Exams (take home for 24 hours, midterm and final have no coding questions)

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):

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.


Please fill the knowledge card attached here, and send it back via email.

Assignment 1 : Due Date Feb 27 , Solution: Click to Download DFS and DLS parts

Important Announcement for Assignment 1: Due Date is postponed, please implement DFS and DLS until March 6 and BFS until Mar 13

Update for the second part of first assignment: Because of the spring break, the due date is updated to Mar 20, together with the second assignment. So, please submit BFS until Mar 20.

Assignment 2: A* Search Problem, Due Date Mar 20 (Extended to Mar 27)

Assignment 3:  Constraint Satisfaction Problem, Due Date Mar 27 (Extended to Apr 3)(Extended to Apr 10) (Extended to May 12)

Assignment 4: Minimax Game Tree, Due Date Apr 3 (Extended to Apr 10)(Extended to Apr 17)(Extended Apr 30)(Extended to May 12)

Assignment 5: Reversi Game, Knowledge Based Agent (Logic) implementation , Due Date Apr 10 (Extended to Apr 24)(Extended Apr 30)(Extended to May 12)

Assignment 6: Machine Learning Assignment for Recognizing Smith College Students (Due Date: Apr 24)(Extended Apr 30)(Extended to May 12)


Important Announcement : All the assignment submissions are extended until May 12 midnight. So you can submit or resubmit all assignments if you want (except the first 2 assignments, that we have already solved at class). I will only grade the latest submission if you have duplicate submissions.