1 Credit Hour Course Intended For Level 3 Term 2 Students
URP Course syllabus
3
2
CSE311
Data Communication
3 Credit Hour Course Intended For Level 3 Term 2 Students
Signal and random processes; Review of Fourier Transform; Hilbert Transform, continuous wave modulation: AM, PM, FM; Sampling theorem; Pulse modulation: PAM, PDM, PPM, PCM, companding, delta modulation, differential PCM; Multiple access techniques: TDM, FDM; Digital modulation: ASK, PSK, BPSK, QPSK, FSK, MSK, constellation, bit error rate (BER); Noise; Echo cancellation; Intersymbol Interference; Concept of channel coding and capacity.
3
2
CSE321
Computer Networks
3 Credit Hour Course Intended For Level 3 Term 2 Students
Protocol hierarchies; Data link control: HLDC; DLL in Internet; DLL of ATM; LAN Protocols: Standards IEEE 802.*; Hubs, Bridges, and Switches, FDDI, Fast Ethernet; Routing algorithm; Congestion control; Internetworking, WAN; Fragmentation; Firewalls; IPV4, IPV6, ARP, RARP, Mobile IP, Network layer of ATM; Transport protocols; Transmission control protocol: connection management, transmission policy, congestion control, timer management; UDP; AAL of ATM; Network security: Cryptography, DES, IDEA, public key algorithm; Authentication; Digital signatures; Gigabit Ethernet; Domain Name System: Name servers; Email and its privacy; SNMP; HTTP; World Wide Web.
3
2
CSE322
Computer Networks Sessional
0.75 Credit Hour Course Intended For Level 3 Term 2 Students
Laboratory works based on CSE 321.
3
2
CSE325
Information System Design
3 Credit Hour Course Intended For Level 3 Term 2 Students
System analysis fundamentals: systems, roles, and development methodologies; Understanding and modeling organizational system; Project management; Information requirements analysis: Interactive methods; Information gathering: Unobtrusive methods; agile modeling and prototyping; The analysis process: Using data flow diagrams; Analyzing systems using data dictionaries; Process specifications and structured decisions; Object oriented systems analysis and design using UML; The essentials of design: Designing effective output, Designing effective input; Designing databases; Human-computer interaction; Quality assurance and implementation: Designing accurate data entry procedures; Quality assurance and implementation.
3
2
CSE326
Information System Design Sessional
0.75 Credit Hour Course Intended For Level 3 Term 2 Students
Sessional based on CSE325.
3
2
CSE329
Machine Learning
3 Credit Hour Course Intended For Level 3 Term 2 Students
Developing machine learning systems: problem formulation, data collection, manipulation and preprocessing, exploratory data analysis and visualization; Deep learning: linear regression as a neural network, simple feedforward networks, forward propagation, backward propagation, computation graphs, numerical stability and initialization; Optimization: batch and stochastic gradient descent (SGD); Convolutional neural networks (CNN): convolution, padding, stride, pooling, modern CNNs (AlexNet, VGG, GoogLeNet, ResNet), batch normalization; Recurrent neural networks (RNN): language modeling with RNNs, modern RNNs, long short-term memory (LSTM), gated recurrent units (GRU), recursive neural networks, sequence-to-sequence (seq2seq) models; Generalization in deep learning: designing neural network architectures, weight decay, dropout; Probabilistic modeling and reasoning: Bayesian networks, exact inference, variable elimination algorithm, approximate inference, direct sampling methods, inference by Markov chain simulation, Gibb’s sampling; Probabilistic reasoning over time: inference in temporal models, hidden Markov model, Kalman filters; Learning probabilistic models: learning with complete data, Bayesian learning, naive Bayes models, generative and discriminative models, generalized linear model; Learning with hidden variables: expectation–maximization (EM) algorithm, mixture models, learning mixtures of Gaussians, K-means clustering, learning hidden Markov models; Dimensionality reduction: principal component analysis (PCA); Recommender systems: collaborative filtering using matrix factorization.
3
2
CSE330
Machine Learning Sessional
0.75 Credit Hour Course Intended For Level 3 Term 2 Students
Laboratory works based on CSE 329.
3
2
CSE450
Capstone Project
1.5 Credit Hour Course Intended For Level 3 Term 2 Students
Solving complex engineering problems related to computer science and engineering.
3
2
HUM347
Ethics in Society and E-Governance
3 Credit Hour Course Intended For Level 3 Term 2 Students
Ethics and society: Ethics: approaches, ethical dimensions of technology, historical perspectives; Categorical imperative: motivation of action; Deviance, delinquency, morals, law, ethics; Ethics in social survey; Online communication: manifest and latent functions; Social group dynamics; Technology and inequality: class and gender issues; Social media: effect on individual autonomy, socialization, privacy, cyberbullying; Dramaturgy: presentation of self; Social interaction: distant learning, work from home; Value system, cultural ethics; Other social issues: individualism, community, minority; Digital Bangladesh and SDGs; Professional ethics: ACM code of ethics and professional conduct, responsibility, liability, loyalty, whistleblowing, trust and reliability in research and testing, conflict of interest; Intellectual property: licensing, copyrights, patents, trade secrets, plagiarism; Environmental impact: energy consumption by computing systems, electronic waste, repairing and recycling. E-Governance: Defining government, institutional framework, democracy, leadership, bureaucracy, accountability and transparency in decision- making, concept of governance, citizen engagement in good governance, e-governance for good governance, e-government and public service delivery, public-private partnership for e-governmen system, legal and ethical issues in e-government, e-government development index (EGDI), ICT policy and smart government.
