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Algorithms & Complexity
Assignments
Assignment 01
01 Abstract Problem
02 High-Level Algorithms and Polynomial Time
03 Degrees of uncertainty
04 Polynomial Time
05 Closure of P under Boolean combinations
06 Nondeterminism by certificates
07 Closure of NP under positive Boolean combination
08 Polynomial-time reductions
09 NP-hardness
10 History of Computing
11 How much harder is solving as compared to just verifying ?
12 P versus NP
13 Hardness for the class NP
14 The first NP-hard problem
15 A Simple reduction - Almost SAT
16 Algorithmic Paradigm to cope with NP-hardness
17 The Stable Matching Problem
18 Stable Matching for allocations within one group
19 What about Stable Matching for allocations between two groups ?
20 How fast can we solve the Stable Matching Problem ?
21 Gale-Shapley Algorithm for STABLE MATCHING
22 What are greedy algorithms ?
23 Dijkstra's Algorithm
24 Minimum Spanning Trees
25 Minimum Spanning Trees via Prim's Algorithm
26 Minimum Spanning Trees via Kruskal's algorithm
27 The Reverse-Delete algorithm for finding MST
28 The Interval Scheduling problem
29 A harder variant of scheduling
30 Greedy algorithm for Partitioning problem
31 What is dynamic programming ?
32 Interval Scheduling problem (without weights)
33 The Weighted Interval Scheduling problem
34 Bellman-Ford Algorithm
35 The Subset Sum Problem
36 Max Independent Set problem on Trees
37 What is divide and conquer ?
38 The MergeSort algorithm
39 Two methods to solve recurrences
40 Counting Number of Inversions
41 How fast can we multiply two n-digit numbers ?
42 Uniform vs No-Uniform models
43 CNF's
44 A first non-uniform class
45 Undecidability
46 Boolean Circuits
47 The EXACT functions
48 A non-symmetric example - Inequality
49 Composing constructions
50 Depth of a circuit
51 Formulas
52 Other gates
Algorithms & Complexity
Artificial Intelligence 2
Exercises
Exercise 01
Exercise 02
01 Uncertainty
02 Bayesian Estimation
03 Maximum A Posteriori Estimation
04 Maximum Likelihood estimation - Frequentist Framework
05 Bernoulli Distribution
06 Binomial Distribution
07 Poisson Distribution
08 PDF vs PMF
09 Urn Problem
10 Likelihood function
11 Maximum Likelihood Estimate and Estimator
12 The First-Order Optimality Condition
13 Gradient Descent
14 Logistic Regression
15 Maximum Likelihood Estimation of Logistic Regression
16 Cost Function for Logistic Regression
17 Main Assumptions in Logistic Regression
18 Bayes rule as a classifier
19 The Naive Bayes Classifier
20 Information Theory
21 Self Information
22 Entropy
23 Joint Entropy and Conditional Entropy
24 Relative Entropy (Kullback-Leibler Divergence)
25 Cross Entropy
26 Mutual Information
27 Decision Tree
28 Gini Index
29 Model Definition
30 Preparation for Test Set Bounds
31 Test Set Bound (Theorem)
32 Training Set Bounds
33 Occam's Razor Bound
34 Estimator
35 Bias, Variance and Noise
36 Bias-Variance Trade-off
37 Overfitting vs Underfitting
38 Regularisation
39 Bootstrapping ensembles
40 Probabilistic Graphical Model
41 Bayesian Networks
42 Formal Meaning of a Bayesian Network
43 Probabilistic Relationships (Standard Structures)
44 Bayesian Network Inference
45 Marginalisation
46 State Space Graph and Search Tree
47 Generic tree search
48 Constraint Satisfaction Problem
49 Minesweeper as a CSP
50 Variety of CSPs
51 Solving CSPs by standard search formulation
52 Tree Search vs Local Search
53 Local Search for CSPs
54 Optimisation
55 Game is a search problem
56 Minimax
57 Game Tree Pruning
AI2
Intelligent Software Engineering
01 Intelligent Data-Driven Bug Report Analysis
02 Intelligent Configuration Performance Learning
03 Intelligent Software Defect Prediction
04 Intelligent Software Testing
05 Software Engineering for AI - System Level Perspectives
06 Intelligent Software Configuration Tuning
07 Evaluation in Intelligent Software Engineering
08 Requirements Engineering for AI-Enabled Systems
09 Software Architecture for AI Systems
Intelligent Software Engineering
Machine Learning
01 Supervised Learning
02 Optimisation in Logistic Regression
03 Nonlinear Transformation in Machine Learning
04 Support Vector Machines
05 Lagrange Duality and SVM Dual Formulation
06 Support Vector Machines - Dual Predictions & Kernels
07 Soft Margin SVM - Primal and Dual Formulations
08 Sequential Minimal Optimisation (SMO)
09 Linear Regression
10 Bayesian Inference and Probabilistic Linear Regression
11 Bayesian Linear Regression
12 Learning Feasibility
13 VC Dimension and Generalisation
14 Bias and Variance Analysis
15 Overfitting and Regression Model Evaluation
16 Regularisation
17 Validation
Machine Learning
Neural Computation
01 What is Neural Computation ?
02 Neurons
03 Perceptron Classifier
04 Perceptron Regressor
05 Optimisation Problem
06 Maximum Likelihood
07 Gradient Descent
08 Multi-Layer Perceptron (MLP) Architecture
09 Computation Graphs
10 The Back-propagation Algorithm
11 Training and Loss Function
12 Generalisation
13 Training Protocols & Ethics
14 Activation Functions
15 Digital Image Representation and MLP Limitations
16 Convolutional Neural Network
17 CNN Structure 4D Data
18 Training Optimisation and Normalisation
19 Regularisation & Transfer Learning
20 Dense Prediction & UNet Architecture
21 Supervised vs. Unsupervised Learning
22 Auto-Encoders
23 Contrastive Learning and SimCLR
24 Generative Models and GANs
25 Diffusion Models
NC
Operating Systems and Systems Programming
01 von Neumann Architecture
02 The CPU
03 Memory Layout of a C program
04 Composite data types as structures
05 Passing Pointers to a Function
06 File handling in C
07 Command line arguments in C
08 Sockets
09 Concurrency using Threads
10 Thread
11 Synchronization mechanism in pthreads
12 Memory Management
13 Page Replacement Algorithms
14 Thrashing
15 Memory Management in the Linux Kernel
16 Interaction between kernel and user program
17 Linux kernel modes
18 A tour of Linux Kernel
19 Process Concept
20 Context Switching
21 Scheduling
22 CPU Scheduling
23 Scheduling Algorithms
24 Scheduling For Multiprocessor Systems
25 Linux Implementation of Scheduling
26 The Critical-Section Problem
27 Solution Criteria to Critical-Section Problem
28 Synchronisation Hardware
29 Inefficient Spinning
30 Semaphores
31 File System
32 Caching
33 Disk Scheduling Algorithms
34 Device Drivers
OSSP
Security and Networks
01 Hashing
02 Kerckhoff's Principle
03 One Time Pads
04 Symmetric Cryptography
05 Padding
06 Block Cipher Modes
07 Probabilistic Encryption
08 Counter Mode (CTR)
09 Known Plain Text Attacks
10 Secure Key Exchange
11 Encryption using RSA
12 IP, TCP, DNS
13 Internet Protocol Stack
14 MAC, IP address, DHCP and ARP
15 ARP Spoofing Attach - Traffic Interception
16 Assumption of Modern Internet
17 Attacks
18 Key Establishment Protocol
19 Needham-Schroeder Public Key Protocol
20 Needham-Schroeder-Lowe Public Key Protocol
21 Forward Secrecy
22 Certificates - Verifying Public Keys
23 Full Station-to-Station Protocol
24 Needham-Schroeder Key Establishment Protocol
25 Some Key Establishment Goals
26 The SSL TLS Protocol
27 X.509 Standard for Certificates
28 Internet Protocol Stack with TLS
29 Self-Signed Certificates
30 Diffie-Hellman in TLS
31 VPNs
32 Onion Routing
33 Network vs Local Injection
34 Fixes - In-band vs Out-band
35 Classifying SQL Injections
36 HTTP & HTTP Communication
37 GET vs POST
38 Cookies - State in a Stateless World
39 Session Hijacking
40 Cross-Site Scripting (XSS)
41 XSS Protections
42 Broken Access Control
43 Cross-site Request Forgery (CSRF)
44 Same-Origin Policy
45 Reverse Engineering
46 Binaries
47 x86-64 Architecture Overview
48 Buffer Overflow
S&N
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Neural Computation
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02 Neurons
02 Neurons
Apr 10, 2026
1 min read
Perceptron aka McCulloch-Pitts Neuron
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