Date | Topic/Slides | Readings | |
---|---|---|---|
1/22 | Introduction to AI and the course | Skim Chapters 1 and 2 | |
1/24 | State Space Search | Section 3.1-3.3 | |
1/27 | Depth-First, Breadth-First Search | Section 3.4 | |
1/29 | Depth-First, Breadth-First, Iterative Deepening Search | Section 3.4 | |
1/31 | A* Search | Sections 3.5-3.6 | |
2/3 | A* Search, Local Search | Section 3.6,4.1 | |
2/5 | Adversarial Search | Sections 5.1-5.3 | |
2/7 | Snow Day | ||
2/10 | Adversarial Search | Sections 5.1-5.3 | |
2/12 | Adversarial Search | Sections 5.1-5.3 | |
2/14 | Propositional Logic | Sections 7.1-7.4 | |
Video Link: click here | |||
2/17 | Propositional Logic | Sections 7.4-7.5 | |
2/19 | Propositional Logic | Sections 7.5 | |
2/21 | Propositional Logic, First-Order Logic | Sections 7.5-7.6 | |
2/26 | First-Order Logic | Sections 8.1-8.2, 9.1-9.2 | |
2/28 | First-Order Logic | Sections 9.1-9.2, 9.5 | |
3/2 | First-Order Logic, Markov-Decision Processes | Sections 9.1-9.2, 9.5, 17.1 | |
3/4 | Markov-Decision Processes | Section 17.1 | |
3/6 | Markov-Decision Processes | Section 17.2 | |
3/9 | Markov-Decision Processes, Reinforcement Learning | Sections 17.2, 22.1, 22.3 | |
Policy iteration example from class | |||
3/11 | Reinforcement Learning | Section 22.3 | |
3/13 | Reinforcement Learning, Multi-Armed Bandits | Section 22.3, Section 17.3 | |
3/16 | Monte Carlo Tree Search | Section 5.4 | |
3/18 | Overview of Machine Learning and Supervised Learning, Linear Regression | Sections 19.1-19.2, 19.6 | |
3/20 | Perceptron | Section 19.6 | |
3/23 | Perceptron, Supervised Learning | Sections 19.6, 19.4 | |
3/25 | Neural Nets | ||
3/27 | Neural Nets | ||
4/6 | Neural Nets | ||
4/8 | Natural Language Processing | ||
4/10 | Natural Language Processing | ||
4/13 | Naive Bayes | Section 20.2 |
Number | Assignment | Due | Late | Solutions | |||
---|---|---|---|---|---|---|---|
Homework 1 | Background knowledge assessment | 1/31 11:59 PM | No late submissions | HW1 Solutions | |||
Homework 2 | Search Algorithms | 2/17 11:59 PM | 2/19 11:59 PM | HW2 Solutions | |||
Homework 3 | Game Trees & Logic | HW3 Written Solutions | |||||
.ipynb file | |||||||
Homework 4 | Learning Approaches |
Absolute integrity is expected of every Cornell student in all academic undertakings. Integrity entails a firm adherence to a set of values, and the values most essential to an academic community are grounded on the concept of honesty with respect to the intellectual efforts of oneself and others. Academic integrity is expected not only in formal coursework situations, but in all University relationships and interactions connected to the educational process, including the use of University resources. ... A Cornell student's submission of work for academic credit indicates that the work is the student's own. All outside assistance should be acknowledged, and the student's academic position truthfully reported at all times. In addition, Cornell students have a right to expect academic integrity from each of their peers.Any distribution or dissemination of course assignment information (such as copies of exams, solutions, etc.) to students not taking the course - whether to students in a subsequent offering of the course, via a website, or any other circmstance - is a violation of the academic intregity policy for this course.