Logistics

• Class meetings: Tuesday and Thrusday 8:00 - 9:15 AM CST, HRBB 113.
• Canvas for maintaining course materials, uploading survey and final report.
• Slack for announcements, Q&A, and discussion.
• For personal logistical questions, contact the instructor via e-mail.

Grading Policy

Quizzes (10%)

• Students are expected to attend classes and take the quiz during the class. There will be 4-5 quizzes throughout the whole semester. Each will take roughly 5-10 mins using Canvas.

Homework assignments (50%)

• There will be 4 homework assignments. Each assignment includes a programming set. Programming in Python 3 is required. Canvas will be used for the submission of the report and code. Students should follow the homework and submission instructions.

Final project (40%)

• Proposal (5%: one-page report + highlight presentation): Students will select a research topic for the final project and give a brief proposal presentation. The presentation should include the motivation, problem setup, baseline methods, potential solutions, and expected outcomes of the proposed project. The project must be relevant to the topics studied in this course (AI in general). The presentation and report (template will be provided) are graded on the efforts and clarity in presenting the ideas of the project.
• Final project presentation (15%): Students are expected to present their final project, covering the motivation, problem setup, proposed approach, experimental results, etc. The presentation is graded on the efforts and clarity in presenting the ideas of the project.
• Final project blog (20%): Students will run experiments and do analysis to explore the research question. The students will then write up the research in the format of a blog post, which will include an explanation of the background material, the new investigations, experimental analysis, and conclusion. The PDF version (or screenshot) of the blog post will be uploaded to Canvas for grading. The blog is graded on the efforts, novelty, completeness, results, and clarity of the final project. The students are encouraged to include plots, animations, and interactive graphics to make the findings clear. Some examples of nice research blog posts will be provided.

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.

 

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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
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
	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
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
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
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
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
13	Tu 2/25	Non-linear regression + details Gradient descent and Newton's method	PRML: 1.1, 1.2.5, 1.5.5 PRML: 3.1
14	Th 2/27	Non-linear regression (cont.)	PRML: 1.1, 1.2.5, 1.5.5 PRML: 3.1
Week 8
15	Tu 3/4	Parameter estimation for probability models + details Probability basics, probability refresher, distribution modeling (parameter estimation, MAP, ML), Bayes rule	PRML: 3.1.3, 5.2.4, 1.2.3, 1.2.4, 2.3, 8.1.1-8.1.3, 8.2, 8.4.1 AI: 13.1-13.5 Review: probability	Assignment 2 due Assignment 3 out
	Th 3/6	Project highlight presentation
Week 9
	Tu 3/11	Spring break (no class)
	Th 3/13	Spring break (no class)
Week 10
16	Tu 3/18	Parameter estimation and Naïve Bayes	PRML: 3.1.3, 5.2.4, 1.2.3, 1.2.4, 2.3, 8.1.1-8.1.3, 8.2, 8.4.1 AI: 13.1-13.5 Review: probability
17	Th 3/20	Graphical model introduction + details Bayesian networks	PRML: 1.2.3, 1.2.4, 2.3, 8.1.1-8.1.3, 8.2, 8.4.1 AI: 13.1-13.5
Week 11
18	Tu 3/25	Graphical model introduction + details Bayesian networks	PRML: 1.2.3, 1.2.4, 2.3, 8.1.1-8.1.3, 8.2, 8.4.1 AI: 13.1-13.5
19	Th 3/27	Unsupervised learning, clustering + details Unsupervised learning overview, K-means/medoids, agglomerative	PRML: 9.1
Week 12
20	Tu 4/1	Supervised learning + details Supervised learning overview, Train/Val/Test, cross-validation, overfitting, KNN, Decision Trees, Random Forest, Boosting/Bagging, Logistic Regression, SVM-lite, Bayesian Classifier	PRML: 1.1, 1.3 AI 18.1 - 18.3	Assignment 3 due Assignment 4 out
21	Th 4/3	Neural networks and deep learning + 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 13
22	Tu 4/8	Transformer, foundation models, large language models
23	Th 4/10	Frontiers: Generative AI, Agentic AI, Physical AI	AI: 22-25
Week 14
24	Tu 4/15	Frontiers: Generative AI, Agentic AI, Physical AI	AI: 22-25
	Th 4/17	Guest Lecture: Prof. Yapeng Tian
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)

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