Texas A&M University

CSCE 625 Artificial Intelligence

Spring 2025

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Course Overview

Description: This graduate-level course will focus on the fundamental concepts and modern methods of artificial intelligence such as search (uninformed, informed, iterative improvement, constraint satisfaction, space/time complexity), game playing (minmax, alpha-beta pruning; knowledge representation and reasoning; propositional logic, first-order logic and automated theorem proving, etc.), planning, uncertainty and probabilistic reasoning, machine learning and deep learning basics. Selected topics include foundation models, generative AI, agentic AI, physical AI, robotics, natural language processing, computer vision, and AI ethics.




Course Information

Instructor: Cheng Zhang

chzhang at tamu dot edu

Office hour: 2-3 PM, Friday

Office: Peterson 321

TA: Fengzhi Guo

fengzh_g at tamu dot edu

Office hour: 1-2 PM, Friday

Office: Peterson 364

Logistics

Grading Policy

  • Quizzes (10%)
  • Homework assignments (50%)
  • Final project (40%)
  • Textbooks

    This course does not mandate any textbook. The lecture slides/videos and other materials provided by the instructor will be sufficient, serving as the primary reference. In addition, the students are recommended to refer to the following textbooks and materials:
  • Stuart Russell and Peter Norvig, Artificial intelligence: a modern approach (3rd edition). Pearson, 2010
  • Christopher M Bishop, Pattern recognition and machine learning. Springer, 2006.
  • Kevin P. Murphy, Machine Learning: A Probabilistic Perspective. The MIT Press, 2012
  • Shai Shalev-Shwartz and Shai Ben-David, Understanding machine learning: From theory to algorithms. Cambridge University Press, 2014.
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep learning. MIT Press, 2016.
  • Ethem Alpaydin, Introduction to Machine Learning. The MIT Press.
  •  



    Class Schedule


    ** class schedule is subject to change **

    AI: Artificial intelligence: a modern approach (3rd edition). Stuart Russel and Peter Norvig

    PRML: Pattern recognition and machine learning. Springer, 2006. Christopher M Bishop

    Lecture Date Topic Additional Reading Note
    Week 1: Overview and AI agent
    1 Tu 1/14 Logistics and course overview [slides] Review: linear algebera, probability, and Python
    2 Th 1/16 AI agent design [slides] AI: 1-2 Drop out due: 1/17
    Week 2: Search agent
    Tu 1/23 Lecture canceled due to inclement weather AI: 3.1-3.5
    AI: 5.1-5.3
    3 & 4 Th 1/16 Uninformed & Informed search [slides] [slides]
    + details BFS, DFC, UCS, Greedy, and A* search
    AI: 3.1-3.5
    AI: 5.1-5.3
    Week 3: Search agent
    5 Tu 1/28 Graph search [slides] AI: 3.1-3.5
    AI: 5.1-5.3
    Assignment 1 out [link]
    6 Th 1/30 Adversarial search [slides] AI: 3.1-3.5
    AI: 5.1-5.3
    Week 4: Learning agent (concept and data)
    7 Tu 2/4 Learning agents and data [slides]
    + details Maching learning overview, application data
    PRML: 1.2.1, 1.2.2, 1.2.4
    PRML: 2.5
    8 Th 2/6 Data, feature, and representation [slides]
    + details Bag of words, histograms
    PRML: 1.2.1, 1.2.2, 1.2.4
    PRML: 2.5
    Week 5: Learning agent (feature and representation)
    9 Tu 2/11 Correlation and normalization [slides]
    + details Non-parametric representations (Parzen), data correlation, Z-score
    PRML: 1.4
    PRML: 12.1, 12.4.3
    Assignment 1 due
    Assignment 2 out [link]
    10 Th 2/13 Dimensionality reduction [slides]
    + details PCA & embedding (T-SNE)
    PRML: 1.4
    PRML: 12.1, 12.4.3
    Week 6: Learning agent (optimization basics)
    11 Tu 2/18 Linear regression [slides]
    + details General parameter estimation techniques
    PRML: 1.1, 1.2.5, 1.5.5
    PRML: 3.1
    12 Th 2/20 Course project topic discussion [slides] Refer to Slack for the recording
    Week 7: Learning agent (optimization basics, deep learning)
    13 Tu 2/25 Non-linear regression [slides]
    + details Gradient descent and Newton's method, optimization basics
    PRML: 1.1, 1.2.5, 1.5.5
    PRML: 3.1
    14 Th 2/27 Neural nets and deep learning basics [slides]
    + details Deep learning introduction with CNN, RNN, GNN basics, perceptron, multi-layer perceptron, backpropagation, training particulars
    PRML: 4.1.7
    PRML: 5.1-5.3
    Week 8: Learning agent (models)
    15 Tu 3/4 Self-attention and Transformers [slides] Attention Is All You Need
    RNN and Transformer (Chapter)
    The Annotated Transformer
    Neural Machine Translation
    Vision Transformers
    Assignment 2 due Assignment 3 out [link]
    Th 3/6 Project highlight presentation
    Week 9
    Tu 3/11 Spring break (no class)
    Th 3/13 Spring break (no class)
    Week 10: Foundation model basics
    16 Tu 3/18 Pre-training [slides]
    17 Th 3/20 Post-training and adaptation [slides]
    Week 11: Generative Agent
    18 Tu 3/25 Deep generative models [slides]
    19 Th 3/27 Model zoo: diffusion, auto-regressive[slides]
    Week 12
    20 Tu 4/1 Assignment 3 due Assignment 4 out
    21 Th 4/3
    Week 13
    22 Tu 4/8
    23 Th 4/10
    Week 14
    24 Tu 4/15
    Th 4/17 Multimodal AI (Prof. Yapeng Tian) Guest lecture
    Week 15
    Tu 4/22 Final presentation Assignment 4 due
    Th 4/24 Final presentation
    Week 16
    Tu 4/29 Final presentation (if needed)
    Th 5/1 Reading day (no class)