Course Duration: 2 years
Semester 1
- Foundations of Artificial Intelligence
- Introduction to AI concepts, history, and applications
- Machine learning basics: algorithms, supervised and
unsupervised learning - Probability and statistics for data science
- Data Mining and Analytics
- Data preprocessing and cleaning
- Exploratory data analysis
- Clustering and association rule mining
- Programming for Data Science
- Python programming fundamentals
- Data manipulation with Pandas
- Visualization with Matplotlib and Seaborn
- Mathematics for Machine Learning
- Linear algebra essentials
- Calculus and optimization
- Probability theory
Semester 2:
- Advanced Machine Learning
- Deep learning fundamentals
- Neural networks architectures
- Convolutional and recurrent neural networks
- Big Data Technologies
- Introduction to distributed computing
- Hadoop and MapReduce
- Spark for large-scale data processing
- Natural Language Processing
- Text preprocessing and tokenization
- Sentiment analysis and language modeling
- Neural network approaches to NLP
- Ethics in AI and Data Science
- Legal and ethical considerations in AI
- Bias and fairness in algorithms
- Privacy and data protection regulations
Semester 3:
- Advanced Data Science
- Time series analysis
- Feature engineering and selection
- Model evaluation and ensemble methods
- Reinforcement Learning
- Markov decision processes
- Q-learning and policy gradients
- Applications in robotics and gaming
- Cloud Computing for AI
- Cloud platforms (AWS, GCP, Azure)
- Deploying and scaling AI models
- Serverless computing for AI applications
- Capstone Project Preparation
- Research methods and project design
- Proposal writing and project planning
Semester 4:
- Capstone Project
- Work on a substantial AI or data science project
- Implementing and evaluating a solution
- Writing a thesis and presenting findings
- Internship (or Electives)
- Optional internship in industry or research
- Alternatively, choose from advanced elective courses:
▪ Computer vision
▪ Bayesian methods in AI
▪ Graphical models
▪ Healthcare analytics
- Professional Development
- Job search strategies
- Interview preparation
- Networking and career pathways in AI and data science
Note: This curriculum blends theoretical knowledge with hands-on experience, preparing students for careers in AI and data science across various industries. It’s essential to incorporate practical projects, internships, and networking opportunities to enhance real-world skills and employability. Additionally, this curriculum aligns with the evolving landscape of AI and data science, emphasizing ethics, big data technologies, and advanced machine learning concepts.