Chapter 1: Introduction: definitions of AI; Turing Test and its limitations
Chapter 2: Intelligent Agents: agent types, their abilities and limitations
Chapter 3: Problem Solving by Searching: problem formulation; search algorithms and their properties; applying search algorithms on small problems; Informed Search; heuristic function design and properties; A* and how it works on very small problems; applying A* to real-world problem/puzzles
Chapter 4: Beyond Classical Search: Local search algorithms (4.1); Online Search (4.5).
Chapter 5: Adversarial Search (5.1 to 5.3): MINMAX algorithm, alpha-beta pruning and how they work on small problems.
Chapter 6: Constraint Satisfaction Problems: problem formulation; backtracking search; solving real-world problems/puzzles using CSP heuristics);
Natural Language Processing (see Lecture notes of Week 6-7. Also read Chapter 22.
Chapter 13: Uncertainty: Bayes' rule and its use, conditional independence (excluding 13.6)
Chapter 14: Probabilistic reasoning (14.1 and 14.2) Bayesian networks.
Also, knowing how to do well the sample midterm, assignment 1-3, and 4 (partial) will greatly improve your chance of doing well in midterm.