From MDPs to Reinforcement-Learning Updates | CMU 10-301 Assignment 8
HW8 connects states, actions, rewards, transitions, and value updates while separating environment dynamics, policy, and estimation error.
learning
345 postsClose the Course with Ensembles, k-Means, PCA, and Recommenders | CMU 10-301 Assignment 9
The final written assignment combines ensembles, clustering, representation, and recommendation to test whether you can choose a learning paradigm from problem structure.
Learn Machine Learning Through Nine Assignments | CMU 10-301 Course Overview
Spring 2026 publishes material for 27 lectures and nine homework bundles; outsiders can do the core work but cannot access Panopto, Piazza, Gradescope, or official homework solutions.
CMU's AI Core Redesign: From 15-281 + 10-315 to 07-280 + 07-380
In 2026, CMU recombined its separate general-AI and SCS machine-learning introductions into the 07-280 → 07-380 sequence. This is a redistribution of content and prerequisites, not a pair of simple course renames.
Bitwise Operators, Conversions, and Masks | Stanford CS107 Lecture 4
Lecture 4 first shows that signed/unsigned conversion can preserve bits while changing meaning, that mixed comparisons may surprise, and how sign extension, zero extension, and truncation alter width. It then derives AND, OR, NOT, XOR, and bitmask idioms for testing, setting, clearing, and combining fields.
Integers, Bytes, and Two's Complement | Stanford CS107 Lecture 3
Lecture 3 starts with 32/64-bit address spaces, derives the ranges of unsigned and two's-complement signed integers, inversion-plus-one, and shared addition hardware, then separates unsigned modular arithmetic from C signed overflow and tests the model against four failure cases.
Bit Shifts, Bit Tricks, and GDB | Stanford CS107 Lecture 5
Lecture 5 extends masks to shifts, power-of-two and popcount tricks, then uses an absolute-value example to expose signed intermediate overflow at INT_MIN. Its second half establishes a GDB workflow around breakpoints, execution control, formatted printing, memory examination, and backtraces.
A First C Program, Binary, and Hexadecimal | Stanford CS107 Lecture 2
Lecture 2 puts C back into its Unix history and development environment: headers, main, printf, argc/argv, ssh, emacs, make, and executables. It then derives 8 bits = 1 byte, 256 byte patterns, and reliable conversion among decimal, binary, and hexadecimal.
From the Course Map to the Unix Command Line | Stanford CS107 Lecture 1
Winter 2026 opens by explaining why CS107 goes below programming-language abstractions: from bytes and memory through assembly and heap allocators. It then lays out the 40/10/20/30 grading structure and closes with a first tour of the Unix command line.
From AI Foundations to Machine Learning: Learning Paths through CS50 AI, CS181, and CS182 | Global AI/CS Course Map
CS50 AI is Harvard's most complete public entry point, but the Summer 2026 course still uses 2020 recordings and assignment assets while the rolling OCW projects have moved to other editions. CS181 Spring 2026 exposes current homework and notes without current recordings; CS182 Fall 2026 has not yet completed an offering.
The Pacman AI Project Lineage: How Berkeley CS188 and CMU 15-281 Restructure the Same Material
CMU 15-281's Search and Games explicitly credits Berkeley's Pacman AI projects. The official course site separately lists a zero-point P0 tutorial and five programming assignments, P1–P5.
From Set Language to Cantor's Diagonal | Stanford CS103 Lecture 0
Starting with elements, subsets, and power sets, this lecture culminates in Cantor's diagonal proof that no set is as large as its own power set.
Building a First Direct Proof from Even and Odd | Stanford CS103 Lecture 1
The even-square and odd-sum examples show how arbitrary choices, assumptions, witnesses, and a want-to-show become a checkable direct proof.
Negation, Contraposition, and Contradiction | Stanford CS103 Lecture 2
This lecture identifies exactly when an implication is false, then turns quantified negation, contraposition, and contradiction into checkable proof tools.
Propositional Logic, Truth Tables, and Equivalence | Stanford CS103 Lecture 3
Propositional logic abstracts English statements into Boolean variables, then uses truth tables to check connectives, translation direction, and equivalences.
Objects, Quantifiers, and Types in First-Order Logic | Stanford CS103 Lecture 4
This lecture extends propositional logic into a language about objects: distinguish constants, predicates, functions, and propositions, then express some and every with existential and universal quantifiers.
First-Order Logic II—Nested Quantifiers, Negation, and Uniqueness | Stanford CS103 Lecture 5
Translate natural language one layer at a time: identify universal and existential forms, then handle quantifier order, negation, restricted quantifiers, and uniqueness.
Functions I, from Definitions to Injection and Surjection Proofs | Stanford CS103 Lecture 6
A function is more than a formula: domain, codomain, totality, and determinism are essential, while the quantifiers defining involutions, injections, and surjections dictate their proofs.
Functions II—Surjections, Assumptions, and Composition | Stanford CS103 Lecture 7
This lecture uses surjections and a proof about birds to separate assuming from proving, then shows that involutions are injective and surjective and carries those ideas into function composition.
Cardinality by Bijections and Cantor's Diagonal Argument | Stanford CS103 Lecture 8
Two sets have equal cardinality when a bijection pairs their elements; Cantor's diagonal set defeats every function from S to its power set by constructing a value it misses.