Jiang Bian, Ph.D.
Assistant Professor
Division of Biomedical Informatics
University of Arkansas for Medical Sciences
4301 W. Markham St. Slot 633-1, Little Rock, AR 72205
Email: jbian [at] uams.edu
Work: (501) 686-5418

Outline

Overview

CPSC 7373 is the graduate level course into the field of Artificial Intelligence in the department of Computer Science at UALR. It starts with some basic elements of AI, such as knowledge representation, interference, machine learning, neural networks, graph theory based network analysis, natural language processing, information retrieval, problem solving, and learning methods in general. And we will quickly dive into various specific research topics using these basic AI elements, such as social network analysis, and graph theoretical analysis of the human brain connectome.

Moreover, we are now in the era of the "big data" revolution where nearly every aspect of computing engineering is being driven by large-data processing and analysis, often in real or near-real time. It is important for the students to gain exposure to big data analytic problems and applications. Especially, this class aims to give the students insight to the basics of cloud computing, and hands-on experiences with the state-of-the-art programming paradigm--MapReduce--for a cloud computing environment to address the computational requirements of the big data problem.

The design of the class does have a strong focus on bioinformatics. The applications and problems presented in the class are derived from the instructor's research in bioinformatics using AI elements, such as the study of functional human brain networks and mining social media content for public health issues.

The preliminary list of topics that will be covered in this class include, but are not limited to:

  • Problem solving - Search
  • Probability, graphical representation, and Bayes network
  • Hidden Markov models, Markov Decision Process, and Reinforcement learning
  • Machine learning (supervised, unsupervised) (e.g., support vector machines, k-mean clustering, etc.)
  • Neural networks
  • Graph theory and network analysis (e.g., human brain network organization)
  • Natural Language Processing and Information Retrieval (e.g., social network analysis)
  • Cloud-computing, Map-Reduce programming paradigm, big data analytic

The final list of topics is subjected to change based on the survey conducted at the beginning of the class, according to the students' background and interests.

Course Materials

  • Textbook: Stuart Russell and Peter Norvig. 2009. Artifcial Intelligence: A Modern Approach (3rd ed.). Prentice Hall Press, Upper Saddle River, NJ, USA. (AIAM 1e, chapter 1 and 2 are online at AIAM 1e)

General readings:

Assignments

Schedule

Tentative schedule of classes and assignments (will be updated regularly)
      Week 0 (8/24 F)
        8/24 Friday:
      • Overview of the course (Syllabus)
      Week 1 (8/27 M, 8/29 W)
        8/29 Wednesday:
      • Lecture: Intelligent Agents (AIAM Chapter 2.1, 2.2, 2.3) [slides]
      • Lecture: Problem Solving by Searching - Part 1 (AIAM Chapter 3.1,3.2) [slides]
      Week 2 (9/3 M Labor Day, 9/5 W)
        9/5 Wednesday: (12:20 pm - 13:35 pm)
      • Lecture: Problem Solving by Searching - Part 2 (AIAM Chapter 3.3-3.6) [slides]
      • Assignment #1:
      Week 3 (9/10 M, 9/12 W)
        9/10 Monday:
      • Lecture: Probability and Bayes Network (AIAM Chapter 13,14) [slides]
      • Assignment #0 Due
        9/12 Wednesday:
      • Lecture: Probabilistic Inference (AIAM Chapter 13,14) [slides]
      Week 4 (9/17 M, 9/19 W)
        9/17 Monday:
      • Lecture: Probabilistic Inference (AIAM Chapter 13,14) [slides]
      • Assignment #2:
      Week 5 (9/24 M, 9/26 W)
        9/24 Monday:
      • Presentation: Deconvolution of fMRI signals, Keith Bush
        9/26 Wednesday:
      • Presentation: Large scale data mining in bioinformatics research, Jiang Bian
      Week 6 (10/01 M, 10/03 W)
        9/24 Monday:
      • Proposal presentation:
        • Application of Reinforcement Learning on Tic Tac Toe Game, Gokce Hazaroglu
        • Twitter Analysis for a given phrase, Naren Kaviti
        • Spades AI, Matt Wilson
      • Course project proposals are due today!
        9/26 Wednesday:
      • Proposal presentation:
        • Disease Detection Method based on Functional MRI Network Model, Jiawei Yuan
        • Online Handwriting Character Recognition via Hidden Markov Model, Xiao Liu
      Week 7 (10/08 M, 10/10 W)
        10/08 Monday:
      • Lecture: Machine Learning - Part 1 [slides]
      • Assignment #2 Due Today!
      Week 8 (10/15 M, 10/17 W)
        10/17 Wednesday:
      • Lecture: Unsupervise Learning - Part 2 [slides]
      Week 9 (10/22 M, 10/24 W)
        10/22 Monday:
      • Lecture: Representation with Logic [slides]
      • Lecture: Planning [slides]
        10/24 Wednesday (Cancelled)
      Week 10 (10/29 M, 10/31 W)
        10/29 Monday:
      • Lecture: Planning with Uncertainty (MDP) [slides]
      Week 11 (11/05 M, 11/07 W)
        11/05 Monday (LAB)
      • Lab
        11/07 Wednesday:
      • Lecture: HMM and Filters [slides]
      Week 12 (11/12 M, 11/14 W)
        11/12 Monday (Cancelled, Veteran's Day)
      • Cancelled
      Week 13 (11/19 M, 11/21 W)
        11/19 Monday
      • Hint for Assignment #5 [slides]
      • Lecture: Natural Language Processing - Part 2 [slides]
        11/21 Wednesday:
      • Lecture: Game [slides]
      Week 14 (11/26 M, 11/28 W)
        11/26 Monday
      • Lecture: Game Theory [slides]
        11/28 Wednesday:
      • LAB
      Week 15 (12/03 M, 12/05 W)
        12/03 Monday
      • Final course project presentation:
        • Application of Reinforcement Learning on Tic Tac Toe Game, Gokce Hazaroglu
        • Twitter Analysis for a given phrase, Naren Kaviti
        • Spades AI, Matt Wilson
      • Final project deliverables (reports + codes) are due today!
        12/05 Wednesday:
      • Final course project presentation:
        • Disease Detection Method based on Functional MRI Network Model, Jiawei Yuan
        • Online Handwriting Character Recognition via Hidden Markov Model, Xiao Liu