📗 -> Introduction to Artificial Intelligence - AI
short mode
not done
tags include #class
description includes AI🔗 Critical Links
🔶 Course Description
Registrar:
Design and implementation of intelligent computer systems. Knowledge representation and organization. Memory and inference. Problem solving. Natural language processing.
Professor Description
This course provides an overview of the foundations, problems, approaches, implementation, and applications of artificial intelligence. Topics covered include problem solving, goal-based and adversarial search, logical, probabilistic, and decision theoretic knowledge representation and inference, decision making, and learning. Through programming assignments that sample these topics, students acquire an understanding of what it means to build rational agents of different sorts as well as applications of AI techniques in language processing, planning, vision. The goal of this course is to learn
- Basic techniques for building intelligent computer systems
- Search, (games,) constraint satisfaction, uncertainty and probability, Bayes Rule, Naïve Bayes, Hidden Markov Models
- Introduction to fundamental concepts in machine learning: linear regression, linear regression classifier, perceptron learning rule
- In depth consideration of the role AI will play in our lives
❗ Important
Instructor:
- Lifu Huang (wilburone.github.io)
- Email: lfuhuang@ucdavis.edu
- NLP, Multimodal Learning, AI
TA:
- Mohammad Beigi
- Email: mbeigi@ucdavis.edu
- Barry Yao
- Email: bmyao@ucdavis.edu
- Zihao Lin
- Email: qzlin@ucdavis.edu
Textbooks
No textbook is required, and below are the recommended books to read:
- Artificial Intelligence: A Modern Approach, 3rd Edition. Norvig & Russell. The sections on machine learning covered in the course are not in the 2nd Edition. Because this course is a prerequisite for machine learning, the 3rd Edition is recommended over the much cheaper 2nd Edition.
- Speech and Language Processing, 3rd Ed. Jurafsky & Martin. available online
📄 Class Material
Week 1 - Agents
03/31 Class/AI Intro - ECS170-L1
04/02 Intelligent Agents - ECS170-L2
04/03 Discussion: - ECS170-D1
04/04 Search - ECS170-L3
- fill out, missed the lecture
Week 2 - Search
04/07 Uniformed Search - ECS170-L4
04/09 Informed Search - ECS170-L5
- HW 1 Release
04/11 Informed Search - ECS170-L6
Week 3 - Games Search
04/14 Games and Adversarial Search - ECS170-L7
04/16 Games and Adversarial Search - ECS170-L8
04/18 Non-Classical Search - ECS170-L9
Week 4 -
04/21 Constraint Satisfaction Problem - ECS170-L10
04/23 Constraint Satisfaction Problem - ECS170-L11
04/25 Propositional Logic - ECS170-L12
Week 5 -
04/28 Propositional Logic - ECS170-L13
04/30 First Order Logic - ECS170-L14
05/02 Propositional Logic - ECS170-L15
Week 6 -
05/05 First Order Logic - ECS170-L16
05/07 Probabilistic Agents - ECS170-L17
05/09 Naive Bayes - ECS170-L18
Week 7
05/12 Naive Bayes (cont) - ECS170-L19
05/14 Bayesian Networks - ECS170-L20
05/16 Bayesian Networks - ECS170-L21
Week 8
05/19 Hidden Markov Models - ECS170-L22
- Review, only sparse notes
05/21 Hidden Markov Models - ECS170-L23 - PDF used for slides? Stanford HMM PDF
05/23 Hidden Markov Models - ECS170-L24
Week 9
05/26 No Class (Holiday)
05/28 HMM Extended and Machine Learning - ECS170-L25
- didnt manage to get the first HMM stuff
05/29 Discussion: - ECS170-D9
05/30 Neural Networks and Transformers - ECS170-L26
Week 10
06/02 Neural Networks and Transformers - ECS170-L27
06/04 Large Language Models - ECS170-L28
06/05 Discussion: - ECS170-D10
ECS170-Final-Prep
Topics Actually Covered
AI Intro - ECS170-AI-Intro
Search - ECS170-Search
Constraint Satisfaction - ECS170-Constraint-Satisfaction
Logic - ECS170-Logic
Probability - ECS170-Probability
Machine Learning - ECS170-Machine-Learning
Initial Class Goals
Search
Games as Search
Constraint Satisfaction Problem
Logic
Probability
Markov Processes
Decision Theoretic Agentics
Intro to AI
NLP