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.
Textbooks
 Artificial Intelligence: A Modern Approach (3rd edition), Stuart Russell, Peter Norvig
 Machine Learning Pattern Recognition, C. M. Bishop
Basic Information
 Time: TR 2:003:15pm
 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 12:301:30pm Hylan lab 301, W 2:003:00pm CSB 603
Course Schedule
 Introduction to AI (1) [syllabus] [slides]
 Machine Learning
 Introduction to machine learning (1)
 Clustering (2) [slides] (Kmeans: Chapter 9.1 ML textbook, Hierarchical clustering: http://www.cs.princeton.edu/courses/archive/spr08/cos424/slides/clustering2.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 18.118.3, AI textbook)  *Linear Regression, Overfitting, and Sparse Learning (* Active topics in AI but Not required in exam, AI Textbook 18.6)
 Midterm, 23:15pm, 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, alphabeta pruning, Nash equilibrium, Optimal mixed strategy (4) [gameplaying] [Nash] (Chapters 5.15.3, 17.517.6, AI textbook)
 Logic
 Propositional logic (2)
 1st order logic (2)
 Other topics in AI (reinforcement learning, semisupervised learning, active learning) [others]
 Guest lecture on HCI by Ehsan, Apr. 21
 No class, Apr. 23
 Tournament, Apr. 28
 Final Exam, 45:15pm, 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
 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]