CSE 3521 Survey of Artificial Intelligence I: Basic Techniques

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems.

Details
Textbook
Grading

Homeworks (20%)

Written homeworks will be very short (one or two exam-style questions) and will be graded in a good/mediocre/incomplete basis. You should be prepared to do regular work each week to keep up with the material and the assignments. The instructor will collect the homework in the classroom. Homework assignments may NOT be turned in late. Homeworks are NOT accepted by email.

Projects (30%)

Programming projects will be in Python, and should be submitted to Carmen by 11:59pm on the day it is due (unless otherwise instructed). Each student will have 3 flexible days to turn in late programming projects throughout the semester. As an example, you could turn in the first project 2 days late and the second project 1 day late without any penalty. After that you will loose 20% for each day the project submission is late. Please email your code to the instructor in case there are any technical issues with submission.

Midterm (20%)

Midterm exam will be close book and notes. The exam date: Wednesday March 06 10:10am-11:30am Sample_1(Solution_1), Sample_2(Solution_2), and basic ML knowledge. Here is midterm pdf file, where q3 is from Berkeley midterm in 2014.

Final Exam (30%)

Final exam will be close book and notes. The exam date: Monday Apr 19 10:10am-11:30am. Here is LeetCode website. Past exams for Berkeley CS188.

Grading Scale: Numerical grades will be mapped to letter grades using the standard OSU policy: 93-100 (A), 90-92.9 (A-), 87-89.9 (B+), 83-86.9 (B), 80-82.9 (B-), 77-79.9 (C+), 73-76.9 (C), 70-72.9 (C-), 67-69.9 (D+), 60-66.9 (D), below 60 (E). These cutoffs represent grade minimums. We may adjust grades upward based on class grade distribution curve.

Regrade Policy: If you believe an error has been made in the grading of your exam, you may resubmit it for a regrade - submit a detailed explanation of which problems you think we marked incorrectly and why. Because we will examine your entire submission in detail, your grade can go up or down as a result of a regrade request.

Drop or Withdraw: detailed OSU policy and instructions here

Resources
  • Piazza (QA, discussion, and announcements)
  • Carmen (project submission and restricted resources)
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    Academic Integrity
    Any assignment or exam that you hand in must be your own work (with the exception of group projects). However, talking with others to better understand the material is strongly encouraged. Copying a solution or letting someone copy your solution is cheating. Everything you hand in must be your own words. Code you hand in must be written by you, with the exception of any code provided as part of the assignment. MOSS (Measure of Software Similarity) will be used routinely to detect plagiarism on programming assignments. Any collaboration during an exam is considered cheating. Any student who is caught cheating will be reported to the Committee on Academic Misconduct. Please don't take a chance - if you are having trouble understanding the material, let us know (asking on Piazza, in class or during office hours), and we will be happy to help.
    Programming Projects
  • Project 0 - Python Tutorial (due 01/14)
  • Project 1 - Uninformed Search (due 01/25)
  • Project 2 - Informed Search (due 02/04)
  • Project 3 - Machine Learning (due 03/22)
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    Homework Assignments (subject to change as the spring 2019 term progresses.)
  • Homework 0 - Learn Python Programming in 7 Days (practice)
  • Homework 1 - Constraint Satisfaction Problems (due 02/15 Solutions)
  • Homework 2 - Naive Bayes in scikit-learn (practice)
  • Homework 3 - Learn TensorFlow, modify the CNN architecture into AlexNet and beat original performance (due 04/17), here is cnn_utils file and dataset file.
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    Anonymous Feedback
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    Schedule (The slides are adapted from Berkeley CS 188, OSU CSE 5525, OSU CSE 5523 and UIUC CS 598.)
    Date Topic Required Reading Further Reading
    01/07 Introduction Textbook-1: 1.1-1.3 What is AI? / Basic Questions
    01/09 Intelligent Agents Textbook-1: 2.3-2.4 Libratus
    01/11 Uninformed Search-1 Textbook-1: 3.1-3.4 BFS in Python
    01/14 Uninformed Search-2 Textbook-1: 3.5 DFS in Java and Python
    01/16 Informed Search-1 Textbook-1: 4.2
    01/18 Informed Search-2 Textbook-1: 4.1 Path Finding Algorithms
    01/21 no class (MLK Day)
    01/23 Informed Search-3 (PPTX) Optimality of A*
    01/25 Talk by Jeniya Tabassum
    01/28 CSP-1 Textbook-1: 3.7 Constraint Satisfaction Problems
    01/30 no class (Wind Chill)
    02/01 CSP-2 CSP Notes
    02/04 CSP-3 Textbook-1: 4.4
    02/06 Search Problems Review-1
    02/08 Search Problems Review-2
    02/11 Naive Bayes-1 Textbook-2: 3.5 Learn Naive Bayes Algorithm
    02/13 Naive Bayes-2 ML vs. MAP
    02/15 Naive Bayes-3 Textbook-2: 5.7.2 Multinomial Naive Bayes
    02/18 Naive Bayes-4
    02/20 Logistic Regression-1 Textbook-2: 8.1-8.2
    02/22 Logistic Regression-2 Textbook-2: 8.3
    02/25 Logistic Regression-3 NB vs. LR
    02/27 Decision Tree
    03/01 Support Vector Machine-1
    03/04 no class (Midterm Prepare)
    03/06 Midterm
    03/08 Support Vector Machine-2 Textbook-2: 14.1-14.5
    03/18 Midterm Review Midterm Statistics
    03/20 Convolutional Neural Networks-1 Textbook-2: 16.5.1 An Intuitive Explanation of CNN
    03/22 Convolutional Neural Networks-2 Understanding Convolutions
    03/25 Convolutional Neural Networks-3 Case Study: CNN architectures
    03/27 Talk by Mounica Maddela
    03/28 Faculty Candidate Talk by Zahra Atiq
    03/29 Recurrent Neural Networks-1 Build RNN from Scratch in Python
    04/01 Recurrent Neural Networks-2 Understanding LSTM Networks
    04/03 Recurrent Neural Networks-3
    04/05 Deep Learning for NLP
    04/08 Machine Learning Review-1
    04/10 Machine Learning Review-2
    04/12 Machine Learning Review-3
    04/15 Machine Learning Review-4
    04/19 Final