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CS161 [Winter2023] Fundamentals of Artificial Intelligence

CS161-Winter2023 Fundamentals of Artificial Intelligence

Overview

This course introduces the design of intelligent agents, including the fundamental problem-solving and knowledge-representation paradigms of artificial intelligence. Topics to be covered include state-space and problem reduction methods, brute-force and heuristic search, two-player games, and recent developments in game AI. For knowledge representation and reasoning, we will cover propositional and first-order logic and their inference algorithms. Finally, the course covers probabilistic approaches to AI, such as Bayesian networks to improve the agent’s performance with experience.

Prerequisites

This course requires knowledge of basic computer science, algorithms and complexity (CS180), and programming principles.

Textbook

Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. (3rd Edition), Pearson 2009.

Programming Language

Python

Logistics

Grading Policy

Grades will be computed based on the following factors:

Schedule

# Date Topics Reading Homework
1 1/10 About Course slides, Introduction: What is AI? slides Chapter 1,2  
2 1/12 Problem solving as search & Uninformed search strategies slides annotated slides Chapter 3  
3 1/17 Uninformed search strategies slides annotated slides Chapter 3 HW1 Out
4 1/19 Informed search strategies slides annotated slides Chapter 3  
5 1/24 Local Search Algorithms slides annotated slides Chapter 4 HW1 Due, HW2 Out
6 1/26 Constraint satisfaction slides annotated slides Chapter 6  
7 1/31 Constraint satisfaction slides annotated slides Chapter 6 HW2 Due, HW3 Out
8 2/2 Constraint satisfaction slides annotated slides Chapter 6  
9 2/7 Game playing slides annotated slides Chapter 5  
10 2/9 Game playing slides annotated slides Chapter 5 Hw4 Out
11 2/14 Propositional logic slides annotated slides Chapter 7 HW3 Due
  2/16 Midterm Exam    
12 2/21 Propositional logic slides annotated slides Chapter 7  
13 2/23 First-order logic: representation slides annotated slides Chapter 8 HW4 Due
14 2/28 First-order logic: representation slides annotated slides Chapter 8 HW5 Out
15 3/2 First-order logic: inference slides annotated slides Chapter 9 HW6 Out
16 3/7 Reasoning under uncertainty slides annotated slides Chapter 11 HW5 Due
17 3/9 Reasoning under uncertainty slides annotated slides Chapter 13  
18 3/14 Bayesian Networks slides annotated slides Chapter 13  
19 3/16 Bayesian Networks slides annotated slides Chapter 13 HW6 Due
  3/22 3:00PM-6:00PM Final Exam    

Academic Integrity Policy

Students are encouraged to read the UCLA Student Conduct Code for Academic Integrity.

Homework

There will be 6 homework assignments during the semester as we cover the corresponding material. Homework consists of both problem solving and programming. The lowest homework score will be dropped for you.

Unless otherwise indicated, you may talk to other students about the homework problems but each student must hand in their own answers and write their own code in the programming part. You also must indicate on each homework with whom you collaborated and cite any other sources you use including Internet websites. Students should never see another student’s solution before submitting their own. Students cannot use old solution sets for this class or solution manual to the textbook under any circumstances.Homework assignments will be submitted through bruinlearn.

Please submit your homework on time. Homework is worth full credit before the due date. It is worth zero credit after the due date.

Quiz

There will be 6 in-class paper-based quiz for the purpose of reviewing the newly learned concepts. The quizzes are open textbook. We will drop the lowest quiz score for you.

Exams

There will be one midterm and then final. The exam is in person and open book and note. You are not allowed to discuss with other people.