Ji Liu

CSC 242, Spring 2015, Artificial Intelligence

Course Description

    This course is a prerequisite for many advanced AI courses. It aims at providing students fundamental concepts and popular algorithms in AI.


  • Artificial Intelligence: A Modern Approach (3rd edition), Stuart Russell, Peter Norvig
  • Machine Learning Pattern Recognition, C. M. Bishop

Basic Information

  • Time: TR 2:00-3:15pm
  • Classroom: MEL 203
  • Lecturer Email: jliu AT cs.rochester.edu
  • Lecturer Office Hour: TR 3:20 - 4:20pm (start from the 2nd week) CSB 614
  • Graduate TA: Tianran Hu (chuankui.hu AT gmail.com); Undergraduate TA: Sean Esterkin (sesterki AT u.rochester.edu) Jacob Bisnett (jbisnett AT u.rochester.edu)
  • TA Office Hour (start from the 3rd week): M 12:30-1:30pm Hylan lab 301, W 2:00-3:00pm CSB 603

Course Schedule

  • Introduction to AI (1) [syllabus] [slides]
  • Machine Learning
  •   Introduction to machine learning (1)
  •   Clustering (2) [slides] (K-means: Chapter 9.1 ML textbook, Hierarchical clustering: http://www.cs.princeton.edu/courses/archive/spr08/cos424/slides/clustering-2.pdf)
  •   Classification (KNN, SVM, probability review, naive bayes, decision tree) (6)
       KNN, SVM [KNN, SVM slides] [other useful materials for SVM] (SVM: Chapter 7.1 ML textbook)
       Probability Review [probability slides]
       Naive Bayes [naive bayes] (Naive Bayes: Chapter 13 AI textbook)
       Decision Tree [DT slides] (Chapter 18.1-18.3, AI textbook)
  •   *Linear Regression, Overfitting, and Sparse Learning (* Active topics in AI but Not required in exam, AI Textbook 18.6)
  • Midterm, 2-3:15pm, Mar. 5, MEL 203 (You can bring a piece of cheating sheet with letter paper size and a calculator in exam; Cellphone is not allowed) [midterm review]
  • Search
  •   Uninformed search -- DFS, BFS, Uniformed cost search, Iterative deepening, Bidirectional search (2) [slides] (AI textbook Chap. 3)
  •   Informed search -- A* algorithm (1) [slides]
  •   Advanced search -- Hill climbing, Simulated annealing, genetic algorithm (2) [slides] (Chapters 4, AI textbook)
  •   Game theory -- Minimax algorithm, alpha-beta pruning, Nash equilibrium, Optimal mixed strategy (4) [gameplaying] [Nash] (Chapters 5.1-5.3, 17.5-17.6, AI textbook)
  • Logic
  •   Propositional logic (2)
  •   1st order logic (2)
  • Other topics in AI (reinforcement learning, semi-supervised learning, active learning) [others]
  • Guest lecture on HCI by Ehsan, Apr. 21
  • No class, Apr. 23
  • Tournament, Apr. 28
  • Final Exam, 4-5:15pm, May 8, MEL 203(Only cover the contents after midterm. You can bring a piece of cheating sheet with letter paper size and a calculator in exam; Cellphone is not allowed) [final review]


  • Homework 1 (Due Feb. 10) [pdf] [A.txt] [B.txt]
  • Homework 2 (Due Feb. 24) [pdf] [data]
  • Homework 3 (Due Mar. 3) [pdf]
  • Homework 4 (Due Mar. 31) [pdf]
  • Homework 5 (Due Apr. 14) [pdf]
  • Homework 6 (Not required. But if you want to earn the bonus points, then you should submit your solution to bonus question before Apr. 28) [pdf]
  • Homework 7 (Team homework. Roughly equal importance as an exam. Multiple deadlines. Tournament on Apr. 28. The final deadline is May 2.) [introduction] [instructions] [code]