Advanced concepts, techniques, and applications of artificial intelligence, including knowledge representation, learning, natural language understanding, and vision.
Date | Topic | Required Reading | Suggested Reading |
---|---|---|---|
8/22 | Course Overview, Reinforcement Learning in NLP Research - Guest Lecture by Alan Ritter | 3.1-3.4 | Microsoft Ms. PacMan |
8/24 | Search Review | 3.5, 3.6 | Pancake Sorting |
8/29 | Game Playing 1 - Minimax (HW0 due) | 5.1, 5.2, 5.3, 5.4 | How Checkers was Solved |
8/31 | Game Playing 2 - Expectimax and Utilities | 5.5, 13.1, 13.2, 16.1, 16.2, 16.3 | Rosen's note on Alpha Beta Pruning |
9/5 | Utilities (cont') and homework recitation | ||
Reinforcement Learning | |||
9/7 | Reinforcement Learning 1 - Markov Decision Processes (HW1 due) | 17.1, 17.2 | |
9/12 | Reinforcement Learning 2 - Value Iteration and Policy Iteration | 17.3 | Real-life examples of Markov Decision Processes |
9/14 | drop deadline | ||
9/14 | Policy Iteration (cont') | system of linear equations, dynamic programming | |
9/19 | Reinforcement Learning 3 - Temporal Difference Learning | 21.1, 21.2, 21.3 | Google AlphaGo |
9/21 | Reinforcement Learning 4 - Q-Learning (HW2 due) | 21.1, 21.2, 21.3 | Reinforcement learning for autonomous helicopter flying |
9/26 | Probability Review (Engineering Expo) | 13.3, 13.4, 13.5, 13.6 | Andrew Moore's tutorial and Jean Walrand's note on Probability |
9/28 | Reinforcement Learning 5 - Function Approximation (HW3 due) | 21.4 | Sutten & Barto's reinforcement learning book (Ch. 5, 6, 13) |
10/3 | Reinforcement Learning 6 - Deep Q-Learning and Policy Gradient | 21.5, 21.6 | Deep Q-Learning |
Reasoning Under Uncertainty | |||
10/5 | Midterm Review, Markov Models | 15.1, 15.2 | |
10/9 | P3 due | ||
10/10 | Hidden Markov Models 1 - Forward algorithm and robot localization | 15.1, 15.2 | |
10/12 | no class (fall break) | ||
10/17 | Midterm (in class - Search, Game, Utilities, MDPs, RL) | ||
10/19 | Hidden Markov Models 2 - Particle filtering [lecture notes] | 15.3, 15.5 | |
10/24 | Hidden Markov Models (cont') and Speech Recognition | 15.3, 15.5 | Haytham Fayek's tutorial on Speech Processing |
10/26 | Bayes Nets 1 - Probabilistic Representations | 14.1, 14.2 | |
10/26 | withdraw deadline | ||
10/31 | NLP Research - Guest Lecture by Micha Elsner (HW6 due) | ||
11/2 | Speech Research - Guest Lecture by Peter Plantinga | ||
11/7 | Bayes Nets 2 - D-Seperation [lecture notes] | 14.1, 14.2 | Olivier Chapelle's talk on Bayesian Network Click Model for Web Search |
11/9 | Bayes Nets 3 - Inference [lecture notes] (P4 due) | 14.4 | Stanford CS228: Probabilistic Graphical Models |
11/14 | Bayes Nets 4 - Sampling | 14.5 | David Blei's talk on Topic Models and User Behavior |
Machine Learning and Special Topics | |||
11/16 | Naive Bayes (HW7 due) | 20.1, 20.2 | |
11/21 | no class (thanksgiving) | ||
11/23 | no class (Columbus day) | ||
11/28 | Naive Bayes (cont') [lecture notes] | Text Classification using Naive Bayes by Hiroshi Shimodaira | |
11/30 | Computer Vision 1 - Applications | 18.6 | Facebook Accessibility |
12/5 | Final Review, Computer Vision 2 - Deep Neural Networks and Visualization | 20.1, 20.2.1, 20.2.2 | Google DeepDream |
12/12 12:00-1:45pm | Final Exam (close book and notes) | ||
9/5 | AI Seminar by Wuwei Lan, 4pm in Dreese 263 (optional) | Neural Networks for Sentence Pair Modeling | |
9/18 | Guest Speaker - Eunsol Choi, 4pm in Dreese 480 (optional) | Ultra-fine Entity Typing | |
9/19 | Guest Speaker - Mark Yatskar, 4:30pm in Dreese 480 (optional) | ||
11/7 | AI Seminar by Mounica Maddela, 4pm in Dreese 263 (optional) | A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification |