The main content of the course consist of selected parts of Chapters 4, 5, 7, 9, 15, 17, 26, 34, 35, scheduled along seven lecture weeks as follows:

• Week I: Lecture on simple recursions and their analysis (4-4.1, 4.3-4.5, 7.1). Overview of amortized analysis (Cartesian tree construction, dynamic arrays 17.4.2).
• Week II : Lecture on more complex recurrences for divide and conquer type of problems: Strassen and linear time median finding (4.2,9.3). [Pseudocode of linear time Select fixed (base case to return j-th smallest element on small array)].
• Week III: Lecture on shortest paths and network flows aiming to introduce to reductions and simple dynamic programming (26-26.3 omitting most proofs).
• Week IV: Lecture on more advanced dynamic programming, like those related to trees (15.2, 15.5) and segmentation (1d clustering)
• Week V: Lecture on NP-hardness without formalities, example reduction SAT->3CNF, approximation algorithms (34 and 35 lightweight)
• Week VI: Lecture on NP-hardness with formalities, including Cook theorem and encodings (34 heavyweight)
• Week VII: Lecture on randomized algorithms touching someway all the previous topics: randomized selection / quicksort, random path in DAG, randomized approximation of MAX-3CNF (5,7 + relevant parts of other chapters)