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: 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] + details Unsupervised learning: K-means, PCA, Autoencoder. Self-supervised learning: contrastive learning, predictive learning.	Representation learning (Chapter) Review paper Lil's blog: contrastive learning DINOv2, CLIP, MAE
17	Th 3/20	Post-training and adaptation [slides] + details Supervised fine-tuning (SFT), parameter-efficient transfer learning (PETL), (visual) instruct tuning, re-parameterization, RLHF	Llama 3 Sebastian's blog Visual prompt tuning Visual prompt Prompt via inpainting Open AI RLHF, o1-preview CoT
Week 11: Generative models
18	Tu 3/25	Deep generative models [slides]	Generative models (Chapter) Generative modeling meets representation learning (Chapter) Conditional generation (Chapter)
19	Th 3/27	Model zoo: diffusion, auto-regressive [slides]	VAE Lil' blog: diffusion models Yang Song's blog Autoregressive Generation
Week 12: Decision making & agents
20	Tu 4/1	Diffusion models (cont.) [slides]	VAE Lil' blog: diffusion models Yang Song's blog Autoregressive Generation	Assignment 3 due
21	Th 4/3	Reinforcement learning & robot learning [slides]	Survey FMs for robot learning SayCan RT-1 3D Diffuser Actor
Week 13: Decision making & agents
22	Tu 4/8	World model [slides]		Assignment 4 out [link]
23	Th 4/10	3D Human Foundation Agents [slides]	Michael Black's talk
Week 14: Decision making & agents
24	Tu 4/15	Language and computer use agents [slides]	Building effective agents Lil' blog: LLM powered autonomous agents Yu Su's blog: language agents MOOC on LLM agents Computer-using agent
	Th 4/17	Bridging Vision and Sound: Audio-Visual Scene Perception and Generation (Prof. Yapeng Tian)		Guest lecture
Week 15
	Tu 4/22	Final presentation
	Th 4/24	Final presentation
Week 16
	Tu 4/29	Final presentation (if needed)		Assignment 4 due
	Th 5/1	Reading day (no class)

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